{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 794,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BEM method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Q = 2000/3    #strength of the source-sheet,stb/d\n",
    "h=25.26        #thickness of local gridblock,ft\n",
    "phi=0.2        #porosity \n",
    "kx=200         #pemerability in x direction,md\n",
    "ky=200         #pemerability in y direction,md\n",
    "kr=kx/ky       #pemerability ratio\n",
    "miu=1          #viscosity,cp\n",
    "\n",
    "Nw=1           #Number of well\n",
    "Qwell_1=2000   #Flow rate of well 1\n",
    "Boundary_V=-400 #boundary velocity ft/day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boundary Discretization\n",
    "we will create a discretization of the body geometry into panels (line segments in 2D). A panel's attributes are: its starting point, end point and mid-point, its length and its orientation. See the following figure for the nomenclature used in the code and equations below.\n",
    "<img src=\"./resources/PanelLocal.png\" width=\"300\">\n",
    "<center>Figure 1. Nomenclature of the boundary element in the local coordinates</center>\n",
    "### Create panel and well class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Panel:\n",
    "    \"\"\"Contains information related to a panel.\"\"\"\n",
    "    def __init__(self, xa, ya, xb, yb):\n",
    "        \"\"\"Creates a panel.\n",
    "        \n",
    "        Arguments\n",
    "        ---------\n",
    "        xa, ya -- Cartesian coordinates of the first end-point.\n",
    "        xb, yb -- Cartesian coordinates of the second end-point.\n",
    "        \"\"\"\n",
    "        self.xa, self.ya = xa, ya\n",
    "        self.xb, self.yb = xb, yb\n",
    "        \n",
    "        self.xc, self.yc = (xa+xb)/2, (ya+yb)/2       # control-point (center-point)\n",
    "        self.length = math.sqrt((xb-xa)**2+(yb-ya)**2)     # length of the panel\n",
    "        \n",
    "        \n",
    "        # orientation of the panel (angle between x-axis and panel)\n",
    "        self.sinalpha=(yb-ya)/self.length\n",
    "        self.cosalpha=(xb-xa)/self.length\n",
    "        \n",
    "        self.Q = 0.                                # source strength\n",
    "        self.U = 0.                                # velocity component\n",
    "        self.V = 0.                                # velocity component\n",
    "        self.P = 0.                                # pressure coefficient\n",
    "\n",
    "class Well:\n",
    "    \"\"\"Contains information related to a panel.\"\"\"\n",
    "    def __init__(self, xw, yw,rw,Q):\n",
    "        \"\"\"Creates a panel.\n",
    "        \n",
    "        Arguments\n",
    "        ---------\n",
    "        xw, yw -- Cartesian coordinates of well source.\n",
    "        Q      -- Flow rate of well source.\n",
    "        rw     -- radius of well source.\n",
    "        \"\"\"\n",
    "        self.xw, self.yw = xw, yw\n",
    "        \n",
    "        self.Q = Q                               # source strength\n",
    "        self.rw = rw                                # velocity component\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a node distribution on the boundary that is refined near the corner with cosspace function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def cosspace(st,ed,N):\n",
    "    N=N+1\n",
    "    AngleInc=numpy.pi/(N-1)\n",
    "    CurAngle = AngleInc\n",
    "    space=numpy.linspace(0,1,N)\n",
    "    space[0]=st\n",
    "    for i in range(N-1):\n",
    "        space[i+1] = 0.5*numpy.abs(ed-st)*(1 - math.cos(CurAngle));\n",
    "        CurAngle += AngleInc\n",
    "    if ed<st:\n",
    "        space[0]=ed\n",
    "        space=space[::-1]\n",
    "    return space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discretize boundary element along the boundary \n",
    "Here we implement BEM in a squre grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "N=80     #Number of boundary element\n",
    "Nbd=20   #Number of boundary element in each boundary\n",
    "Dx=1.    #Grid block length in X direction\n",
    "Dy=1.    #Gird block lenght in Y direction\n",
    "\n",
    "#Create the array\n",
    "x_ends = numpy.linspace(0, Dx, N)    # computes a 1D-array for x\n",
    "y_ends = numpy.linspace(0, Dy, N)    # computes a 1D-array for y\n",
    "interval=cosspace(0,Dx,Nbd)\n",
    "rinterval=cosspace(Dx,0,Nbd)\n",
    "#interval=numpy.linspace(0,1,Nbd+1)\n",
    "#rinterval=numpy.linspace(1,0,Nbd+1)\n",
    "\n",
    "#Define the rectangle boundary\n",
    "\n",
    "\n",
    "for i in range(Nbd):\n",
    "    x_ends[i]=0\n",
    "    y_ends[i]=interval[i]\n",
    "\n",
    "for i in range(Nbd):\n",
    "    x_ends[i+Nbd]=interval[i]\n",
    "    y_ends[i+Nbd]=Dy\n",
    "    \n",
    "for i in range(Nbd):\n",
    "    x_ends[i+Nbd*2]=Dx\n",
    "    y_ends[i+Nbd*2]=rinterval[i]\n",
    "    \n",
    "for i in range(Nbd):\n",
    "    x_ends[i+Nbd*3]=rinterval[i]\n",
    "    y_ends[i+Nbd*3]=0\n",
    "    \n",
    "x_ends,y_ends=numpy.append(x_ends, x_ends[0]), numpy.append(y_ends, y_ends[0])\n",
    "\n",
    "#Define the panel\n",
    "panels = numpy.empty(N, dtype=object)\n",
    "for i in range(N):\n",
    "    panels[i] = Panel(x_ends[i], y_ends[i], x_ends[i+1], y_ends[i+1])\n",
    "    \n",
    "    \n",
    "#Define the well\n",
    "wells = numpy.empty(Nw, dtype=object)\n",
    "\n",
    "wells[0]=Well(Dx/2,Dy/2,0.025,Qwell_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#for i in range(N):\n",
    " #   print(\"Panel Coordinate (%s,%s) sina,cosa (%s,%s) \" % (panels[i].xc,panels[i].yc,panels[i].sinalpha,panels[i].cosalpha))\n",
    "#print(\"Well Location (%s,%s) radius: %s Flow rate:%s  \" % (wells[0].xw,wells[0].yw,wells[0].rw,wells[0].Q))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot boundary elements and wells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19cb8278>"
      ]
     },
     "execution_count": 800,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVUAAAFHCAYAAAAREt++AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncxjBFRzsjQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b4c18d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the panel\n",
    "%matplotlib inline\n",
    "\n",
    "val_x, val_y = 0.3, 0.3\n",
    "x_min, x_max = min(panel.xa for panel in panels), max(panel.xa for panel in panels)\n",
    "y_min, y_max = min(panel.ya for panel in panels), max(panel.ya for panel in panels)\n",
    "x_start, x_end = x_min-val_x*(x_max-x_min), x_max+val_x*(x_max-x_min)\n",
    "y_start, y_end = y_min-val_y*(y_max-y_min), y_max+val_y*(y_max-y_min)\n",
    "\n",
    "size = 5\n",
    "pyplot.figure(figsize=(size, (y_end-y_start)/(x_end-x_start)*size))\n",
    "pyplot.grid(True)\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "pyplot.xlim(x_start, x_end)\n",
    "pyplot.ylim(y_start, y_end)\n",
    "\n",
    "pyplot.plot(numpy.append([panel.xa for panel in panels], panels[0].xa), \n",
    "         numpy.append([panel.ya for panel in panels], panels[0].ya), \n",
    "         linestyle='-', linewidth=1, marker='o', markersize=6, color='#CD2305');\n",
    "pyplot.scatter(wells[0].xw,wells[0].yw,s=100,alpha=0.5)\n",
    "\n",
    "pyplot.legend(['panels', 'Wells'], \n",
    "           loc=1, prop={'size':12})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boundary element implementation\n",
    "<img src=\"./resources/BEMscheme2.png\" width=\"400\">\n",
    "<center>Figure 2. Representation of a local gridblock with boundary elements</center>\n",
    "\n",
    "\n",
    "\n",
    "Generally, the influence of all the j panels on the i BE node can be expressed as follows:\n",
    "\n",
    "\\begin{matrix}\n",
    "{{c}_{ij}}{{p}_{i}}+{{p}_{i}}\\int_{{{s}_{j}}}{{{H}_{ij}}d{{s}_{j}}}=({{v}_{i}}\\cdot \\mathbf{n})\\int_{{{s}_{j}}}{{{G}_{ij}}}d{{s}_{j}}\n",
    "\\end{matrix}\n",
    "Where,\n",
    "\n",
    "${{c}_{ij}}$  is the free term, cased by source position.\n",
    "\n",
    "\n",
    "<center>${{c}_{ij}}=\\left\\{ \\begin{matrix}\n",
    "   \\begin{matrix}\n",
    "   1 & \\text{source j on the internal domain}  \\\\\n",
    "\\end{matrix}  \\\\\n",
    "   \\begin{matrix}\n",
    "   0.5 & \\text{source j on the boundary}  \\\\\n",
    "\\end{matrix}  \\\\\n",
    "   \\begin{matrix}\n",
    "   0 & \\text{source j on the external domain}  \\\\\n",
    "\\end{matrix}  \\\\\n",
    "\\end{matrix} \\right.$</center>\n",
    "\n",
    "$\\int_{{{s}_{j}}}{{{H}_{ij}}d{{s}_{j}}\\text{ }}$ is the integrated effect of the boundary element source i on the resulting normal flux at BE node j. \n",
    "\n",
    "$\\int_{{{s}_{j}}}{{{G}_{ij}}}d{{s}_{j}}$ is the is the integrated effect of the boundary element source i on the resulting pressure at BE node j\n",
    "\n",
    "### Line segment source solution for pressure and velocity (Derived recently)\n",
    "\n",
    "The integrated effect can be formulated using line segment source solution, which givs:\n",
    "\n",
    "\\begin{equation}\n",
    "\\int_{{{s}_{j}}}{{{G}_{ij}}}d{{s}_{j}}=B{{Q}_{w}}=P({{{x}'}_{i}},{{{y}'}_{i}})=-\\frac{70.60\\mu }{h\\sqrt{{{k}_{x}}{{k}_{y}}}}\\int_{t=0}^{t={{l}_{j}}}{\\ln \\left\\{ {{({x}'-t\\cos {{\\alpha }_{j}})}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{({y}'-t\\sin {{\\alpha }_{j}})}^{2}} \\right\\}dt}\\cdot {{Q}_{w}}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\int_{{{s}_{j}}}{{{H}_{ij}}d{{s}_{j}}\\text{ }}={{v}_{i}}(s)\\cdot {{\\mathbf{n}}_{i}}=-{{u}_{i}}\\sin {{\\alpha }_{i}}+{{v}_{i}}\\cos {{\\alpha }_{i}}\n",
    "\\end{equation}\n",
    "\n",
    "Where,\n",
    "\n",
    "\\begin{equation}\n",
    "u\\left( {{{{x}'}}_{i}},{{{{y}'}}_{i}} \\right)={{A}_{u}}{{Q}_{j}}=\\frac{0.8936}{h\\phi }\\sqrt{\\frac{{{k}_{x}}}{{{k}_{y}}}}\\int_{t=0}^{t={{l}_{j}}}{\\frac{{{{{x}'}}_{i}}-t\\cos {{\\alpha }_{j}}}{{{\\left( {{{{x}'}}_{i}}-t\\cos {{\\alpha }_{j}} \\right)}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{({{{{y}'}}_{i}}-t\\sin {{\\alpha }_{j}})}^{2}}}dt}\\cdot {{Q}_{j}}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "v\\left( {{{{x}'}}_{i}},{{{{y}'}}_{i}} \\right)={{A}_{v}}{{Q}_{j}}=\\frac{0.8936}{h\\phi }\\sqrt{\\frac{{{k}_{x}}}{{{k}_{y}}}}\\int_{t=0}^{t={{l}_{j}}}{\\frac{{{{{y}'}}_{i}}-t\\sin {{\\alpha }_{j}}}{{{\\left( {{{{x}'}}_{i}}-t\\cos {{\\alpha }_{j}} \\right)}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{({{{{y}'}}_{i}}-t\\sin {{\\alpha }_{j}})}^{2}}}dt}\\cdot {{Q}_{j}}\n",
    "\\end{equation}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line segment source Integration function (Bij and Aij)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 801,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Panel infuence factor Bij\n",
    "def InflueceP(x, y, panel):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "#Transfer global coordinate point(x,y) to local coordinate\n",
    "    x=x-panel.xa\n",
    "    y=y-panel.ya\n",
    "    L1=panel.length\n",
    "    \n",
    "#Calculate the pressure and velocity influence factor\n",
    "    a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
    "    b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
    "    c=y*panel.cosalpha-x*panel.sinalpha\n",
    "    dp=70.6*miu/h/math.sqrt(kx*ky)\n",
    "    Cp = dp/a*(\n",
    "                             (\n",
    "                              b*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)\n",
    "                             -L1*a*math.log((x-L1*panel.cosalpha)**2+kr*(y-L1*panel.sinalpha)**2)\n",
    "                             +2*math.sqrt(kr)*c*math.atan((b-a*L1)/math.sqrt(kr)/c)\n",
    "                             )\n",
    "                             -\n",
    "                             (\n",
    "                               b*math.log(x**2+kr*y**2)\n",
    "                               +2*math.sqrt(kr)*c*math.atan((b)/math.sqrt(kr)/c)\n",
    "                             )         \n",
    "                )\n",
    "    #debug\n",
    "    #print(\"a: %s b:%s c:%s  \" % (a,b,c))\n",
    "    #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
    "    #print(\"Magic angle:%s\"% angle)\n",
    "    return Cp\n",
    "\n",
    "def InflueceU(x, y, panel):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "#Transfer global coordinate point(x,y) to local coordinate\n",
    "    x=x-panel.xa\n",
    "    y=y-panel.ya\n",
    "    L1=panel.length\n",
    "\n",
    "#Calculate the pressure and velocity influence factor\n",
    "    a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
    "    b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
    "    c=y*panel.cosalpha-x*panel.sinalpha\n",
    "    dv=-0.4468/h/phi*math.sqrt(kx/ky)\n",
    "    Cu = dv/a*(\n",
    "                             ( \n",
    "                            panel.cosalpha*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)+ 2*math.sqrt(kr)*panel.sinalpha*math.atan((a*L1-b)/math.sqrt(kr)/c) \n",
    "                             )\n",
    "                            -\n",
    "                             (\n",
    "                            panel.cosalpha*math.log(x**2+kr*y**2)+2*math.sqrt(kr)*panel.sinalpha*math.atan((-b)/math.sqrt(kr)/c)\n",
    "                             )    \n",
    "                     )  \n",
    "    #print(\"a: %s b:%s c:%s  \" % (a,b,c))\n",
    "    #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
    "    #print(\"Magic angle:%s\"% angle)\n",
    "    return Cu\n",
    "\n",
    "def InflueceV(x, y, panel):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "#Transfer global coordinate point(x,y) to local coordinate\n",
    "    x=x-panel.xa\n",
    "    y=y-panel.ya\n",
    "    L1=panel.length\n",
    "\n",
    "#Calculate the pressure and velocity influence factor\n",
    "    a=panel.cosalpha**2+kr*panel.sinalpha**2\n",
    "    b=x*panel.cosalpha+kr*panel.sinalpha*y\n",
    "    c=y*panel.cosalpha-x*panel.sinalpha\n",
    "    dv=-0.4468/h/phi*math.sqrt(kx/ky)\n",
    "    Cv = dv/a*(\n",
    "                             ( \n",
    "                            panel.sinalpha*math.log(x**2-2*b*L1+a*L1**2+kr*y**2)+ 2*math.sqrt(1/kr)*panel.cosalpha*math.atan((b-a*L1)/math.sqrt(kr)/c) \n",
    "                             )\n",
    "                            -\n",
    "                             (\n",
    "                            panel.sinalpha*math.log(x**2+kr*y**2)+2*math.sqrt(1/kr)*panel.cosalpha*math.atan((b)/math.sqrt(kr)/c)\n",
    "                             )    \n",
    "                     )    \n",
    "    #print(\"a: %s b:%s c:%s  \" % (a,b,c))\n",
    "    #angle=math.atan((b-a*L1)/math.sqrt(kr)/c)*180/numpy.pi\n",
    "    #print(\"Magic angle:%s\"% angle)\n",
    "\n",
    "    return Cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Well source function\n",
    "\n",
    "### Line source solution for pressure and velocity (Datta-Gupta, 2007)\n",
    "\n",
    "\\begin{equation}\n",
    "P(x,y)=B{{Q}_{w}}=-\\frac{70.60\\mu }{h\\sqrt{{{k}_{x}}{{k}_{y}}}}\\ln \\left\\{ {{(x-{{x}_{w}})}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{(y-{{y}_{w}})}^{2}} \\right\\}{{Q}_{w}}+{{P}_{avg}}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{\\partial P}{\\partial x}=u=\\frac{0.8936}{h\\phi }\\sqrt{\\frac{{{k}_{x}}}{{{k}_{y}}}}\\sum\\limits_{k=1}^{{{N}_{w}}}{{{Q}_{k}}}\\frac{x-{{x}_{k}}}{{{\\left( x-{{x}_{k}} \\right)}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{(y-{{y}_{k}})}^{2}}}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "\\frac{\\partial P}{\\partial y}=v=\\frac{0.8936}{h\\phi }\\sqrt{\\frac{{{k}_{x}}}{{{k}_{y}}}}\\sum\\limits_{k=1}^{{{N}_{w}}}{{{Q}_{k}}}\\frac{y-{{y}_{k}}}{{{\\left( x-{{x}_{k}} \\right)}^{2}}+\\frac{{{k}_{x}}}{{{k}_{y}}}{{(y-{{y}_{k}})}^{2}}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Well influence factor\n",
    "def InflueceP_W(x, y, well):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "    dp=-70.6*miu/h/math.sqrt(kx*ky)\n",
    "    Cp=dp*math.log((x-well.xw)**2+kr*(y-well.yw)**2)\n",
    "    return Cp\n",
    "\n",
    "def InflueceU_W(x, y, well):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "    dv=0.8936/h/phi*math.sqrt(kx/ky)\n",
    "    Cu=dv*(x-well.xw)/((x-well.xw)**2+kr*(y-well.yw)**2)\n",
    "    return Cu\n",
    "\n",
    "def InflueceV_W(x, y, well):\n",
    "    \"\"\"Evaluates the contribution of a panel at one point.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    x, y -- Cartesian coordinates of the point.\n",
    "    panel -- panel which contribution is evaluated.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Integral over the panel of the influence at one point.\n",
    "    \"\"\"\n",
    "    dv=0.8936/h/phi*math.sqrt(kx/ky)\n",
    "    Cv=dv*(y-well.yw)/((x-well.xw)**2+kr*(y-well.yw)**2)\n",
    "    return Cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#InflueceV(0.5,1,panels[3])\n",
    "#InflueceP(0,0.5,panels[0])\n",
    "#InflueceU(0,0.5,panels[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BEM function solution\n",
    "Generally, the influence of all the j panels on the i BE node can be expressed as follows:\n",
    "\n",
    "\\begin{matrix}\n",
    "{{c}_{ij}}{{p}_{i}}+{{p}_{i}}\\int_{{{s}_{j}}}{{{H}_{ij}}d{{s}_{j}}}=({{v}_{i}}\\cdot \\mathbf{n})\\int_{{{s}_{j}}}{{{G}_{ij}}}d{{s}_{j}}\n",
    "\\end{matrix}\n",
    "\n",
    "Applying boundary condition along the boundary on above equation, a linear systsem can be constructed as follows:\n",
    "\n",
    "\\begin{matrix}\n",
    "\\left[ {{{{H}'}}_{ij}} \\right]\\left[ {{P}_{i}} \\right]=\\left[ {{G}_{ij}} \\right]\\left[ {{v}_{i}}\\cdot \\mathbf{n} \\right]\n",
    "\\end{matrix}\n",
    "\n",
    "!!!!!MY IMPLEMENTATION MAY HAS SOME PROBLEM HERE!!!!!!\n",
    "\n",
    "All the integration solution can be evaluated except on itself. Where,\n",
    "\n",
    "<center>$\n",
    "\\left[ {{{{H}'}}_{ij}} \\right]=\\left\\{ \\begin{matrix}\n",
    "   \\begin{matrix}\n",
    "   {{H}_{ij}} & i\\ne j  \\\\\n",
    "\\end{matrix}  \\\\\n",
    "   \\begin{matrix}\n",
    "   {{H}_{ij}}+\\frac{1}{2} & i=j  \\\\\n",
    "\\end{matrix}  \\\\\n",
    "\\end{matrix} \\right.\n",
    "$</center>\n",
    "\n",
    "<img src=\"./resources/BEMscheme.png\" width=\"400\">\n",
    "<center>Figure 3. Representation of coordinate systems and the principle of superstition with well source and boundary element source </center>\n",
    "\n",
    "As shown in Fig.3, the pressure and velocity at any point i in the local gridblock can be determined using Eqs. below. Applying principle of superposition for each BE node along the boundary (Fig. 3), boundary condition can be written as follows:\n",
    "\n",
    "\\begin{matrix}\n",
    "   {{P}_{i}}(s)=\\sum\\limits_{j=1}^{M}{{{B}_{ij}}{{Q}_{j}}} & \\text{constant pressure boundary}  \\\\\n",
    "\\end{matrix}\n",
    "\n",
    "\\begin{matrix}\n",
    "   {{v}_{i}}(s)\\cdot {{\\mathbf{n}}_{i}}=\\sum\\limits_{j=1}^{M}{{{A}_{ij}}{{Q}_{j}}} & \\text{constant flux boundary}  \\\\\n",
    "\\end{matrix}\n",
    "\n",
    "\n",
    "The Pi and v ·n are the konwn boundary codition. The flow rate(strength) of boundary elements in Hij and Gij are the only unknown terms. \n",
    "So we could rearrange the matrix above as linear system:\n",
    "\n",
    "<center>$\n",
    "{{\\left[ \\begin{matrix}\n",
    "   {{A}_{ij}}  \\\\\n",
    "   {{B}_{ij}}  \\\\\n",
    "\\end{matrix} \\right]}_{N\\times N}}{{\\left[ \\begin{matrix}\n",
    "   {{Q}_{j}}  \\\\\n",
    "   {{Q}_{j}}  \\\\\n",
    "\\end{matrix} \\right]}_{N\\times 1}}={{\\left[ \\begin{matrix}\n",
    "   -{{u}_{i}}\\sin {{\\alpha }_{i}}+{{v}_{i}}\\cos {{\\alpha }_{i}}  \\\\\n",
    "   {{P}_{i}}  \\\\\n",
    "\\end{matrix} \\right]}_{N\\times 1}}\n",
    "$</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_matrix(panels):\n",
    "    \"\"\"Builds the source matrix.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    panels -- array of panels.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    A -- NxN matrix (N is the number of panels).\n",
    "    \"\"\"\n",
    "    N = len(panels)\n",
    "    A = numpy.empty((N, N), dtype=float)\n",
    "    #numpy.fill_diagonal(A, 0.5)\n",
    "    \n",
    "    for i, p_i in enumerate(panels): #target nodes\n",
    "        for j, p_j in enumerate(panels): #BE source\n",
    "            #if i != j:   ###Matrix construction\n",
    "                if i>=0 and i<Nbd or i>=3*Nbd and i<4*Nbd:     \n",
    "                    A[i,j] = -p_j.sinalpha*InflueceU(p_i.xc, p_i.yc, p_j)+p_j.cosalpha*InflueceV(p_i.xc, p_i.yc, p_j)\n",
    "                    #A[i,j] = InflueceP(p_i.xc, p_i.yc, p_j)\n",
    "                if i>=Nbd and i<2*Nbd or i>=2*Nbd and i<3*Nbd:     \n",
    "                    A[i,j] = -p_j.sinalpha*InflueceU(p_i.xc, p_i.yc, p_j)+p_j.cosalpha*InflueceV(p_i.xc, p_i.yc, p_j)\n",
    "                    #A[i,j] = InflueceP(p_i.xc, p_i.yc, p_j)\n",
    "\n",
    "    return A\n",
    "\n",
    "def build_rhs(panels):\n",
    "    \"\"\"Builds the RHS of the linear system.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    panels -- array of panels.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    b -- 1D array ((N+1)x1, N is the number of panels).\n",
    "    \"\"\"\n",
    "    b = numpy.empty(len(panels), dtype=float)\n",
    "    \n",
    "    \n",
    "    for i, panel in enumerate(panels):\n",
    "        V_well=( -panel.sinalpha*Qwell_1*InflueceU_W(panel.xc, panel.yc, wells[0])+panel.cosalpha*Qwell_1*InflueceV_W(panel.xc, panel.yc, wells[0]) )\n",
    "        if i>=0 and i<Nbd:     \n",
    "            b[i]=0+V_well\n",
    "            #b[i]=4000\n",
    "            #b[i]=84\n",
    "        if i>=Nbd and i<2*Nbd:\n",
    "            b[i]=-V_well\n",
    "            #b[i]=-42\n",
    "        if i>=2*Nbd and i<3*Nbd: \n",
    "            b[i]=-V_well\n",
    "            #b[i]=-42\n",
    "        if i>=3*Nbd and i<4*Nbd:\n",
    "            b[i]=0+V_well\n",
    "            #b[i]=84\n",
    "    return b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:66: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "d:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:70: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "d:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:102: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "d:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:106: RuntimeWarning: divide by zero encountered in double_scalars\n"
     ]
    }
   ],
   "source": [
    "#Qwell_1=300   #Flow rate of well 1\n",
    "#Boundary_V=-227 #boundary velocity ft/day\n",
    "\n",
    "A = build_matrix(panels)                    # computes the singularity matrix\n",
    "b = build_rhs(panels)                       # computes the freestream RHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# solves the linear system\n",
    "Q = numpy.linalg.solve(A, b)\n",
    "\n",
    "for i, panel in enumerate(panels):\n",
    "    panel.Q = Q[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Visulize the pressure and velocity field\n",
    "\n",
    "#Define meshgrid\n",
    "Nx, Ny = 50, 50                  # number of points in the x and y directions\n",
    "x_start, x_end = -0.01, 1.01            # x-direction boundaries\n",
    "y_start, y_end = -0.01, 1.01            # y-direction boundaries\n",
    "x = numpy.linspace(x_start, x_end, Nx)    # computes a 1D-array for x\n",
    "y = numpy.linspace(y_start, y_end, Ny)    # computes a 1D-array for y\n",
    "X, Y = numpy.meshgrid(x, y)               # generates a mesh grid\n",
    "\n",
    "#Calculate the velocity and pressure field\n",
    "p = numpy.empty((Nx, Ny), dtype=float)\n",
    "u = numpy.empty((Nx, Ny), dtype=float)\n",
    "v = numpy.empty((Nx, Ny), dtype=float)\n",
    "\n",
    "#for i, panel in enumerate(panels):\n",
    "    #panel.Q = 0.\n",
    "\n",
    "#panels[0].Q=100\n",
    "#panels[5].Q=100\n",
    "#Qwell_1=400\n",
    "\n",
    "\n",
    "for i in range(Nx):\n",
    "    for j in range(Ny):\n",
    "        p[i,j] =sum([p.Q*InflueceP(X[i,j], Y[i,j], p) for p in panels])+Qwell_1*InflueceP_W(X[i,j], Y[i,j], wells[0])\n",
    "        u[i,j] =sum([p.Q*InflueceU(X[i,j], Y[i,j], p) for p in panels])+Qwell_1*InflueceU_W(X[i,j], Y[i,j], wells[0])\n",
    "        v[i,j] =sum([p.Q*InflueceV(X[i,j], Y[i,j], p) for p in panels])+Qwell_1*InflueceV_W(X[i,j], Y[i,j], wells[0])\n",
    "        #p[i,j] =sum([p.Q*InflueceP(X[i,j], Y[i,j], p) for p in panels])\n",
    "        #u[i,j] =sum([p.Q*InflueceU(X[i,j], Y[i,j], p) for p in panels])\n",
    "        #v[i,j] =sum([p.Q*InflueceV(X[i,j], Y[i,j], p) for p in panels])\n",
    "        #p[i,j] =Qwell_1*InflueceP_W(X[i,j], Y[i,j], wells[0])\n",
    "        #u[i,j] =Qwell_1*InflueceU_W(X[i,j], Y[i,j], wells[0])\n",
    "        #v[i,j] =Qwell_1*InflueceV_W(X[i,j], Y[i,j], wells[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 808,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAGOCAYAAABfSp5jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12120b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots the streamlines\n",
    "%matplotlib inline\n",
    "\n",
    "size = 6\n",
    "pyplot.figure(figsize=(size, size))\n",
    "pyplot.grid(True)\n",
    "pyplot.title('Streamline field')\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "pyplot.xlim(-0.2, 1.2)\n",
    "pyplot.ylim(-0.2, 1.2)\n",
    "\n",
    "\n",
    "pyplot.plot(numpy.append([panel.xa for panel in panels], panels[0].xa), \n",
    "         numpy.append([panel.ya for panel in panels], panels[0].ya), \n",
    "         linestyle='-', linewidth=1, marker='o', markersize=6, color='#CD2305');\n",
    "stream =pyplot.streamplot(X, Y, u, v,density=2, linewidth=1, arrowsize=1, arrowstyle='->') #streamline\n",
    "#cbar=pyplot.colorbar(orientation='vertical')\n",
    "\n",
    "#equipotential=pyplot.contourf(X, Y, p1, extend='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 809,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x22b3b5f8>"
      ]
     },
     "execution_count": 809,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbIAAAGOCAYAAADyy4bKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZoXMGGNMXLNCZowxJq5ZITPGGBPX/j82KbS6WYxbxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c40b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = 7\n",
    "pyplot.figure(figsize=(size, size-1))\n",
    "pyplot.title('Pressure field')\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "pyplot.xlim(0, 1)\n",
    "pyplot.ylim(0, 1)\n",
    "\n",
    "pyplot.contour(X, Y, p, 15, linewidths=0.5, colors='k')\n",
    "pyplot.contourf(X, Y, p, 15, cmap='rainbow',\n",
    "                  vmax=abs(p).max(), vmin=-abs(p).max())\n",
    "pyplot.colorbar()  # draw colorbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1c9c09b0>"
      ]
     },
     "execution_count": 810,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbcAAAGOCAYAAAAU4k2OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XeYXFX5wPHvO2XLbC/Zlp6QBBICSYAQepAamlgQEEEU\nfyoioqIgggI2igXEgqCoVBERJQgiAobQQgkhhSSkt93sJtned2bu+/tjJskk2c3uZsudmX0/z7NP\nds499973bnbnnXPuOeeKqmKMMcYkE4/bARhjjDH9zZKbMcaYpGPJzRhjTNKx5GaMMSbpWHIzxhiT\ndCy5GWOMSTqW3ExCEpFUEXFEpGwAjj1JRIJ9PEaKiDSKSEk/xTRFRBaLSIOIfEFE/iQi3+rhvltF\n5Ngutp0hIqv7I0Zj4oklN9Nvom/mDdGvsIi0xJRd3M2+B/Im2+kkzegb/32dlB8tIs0iknmgx+5x\nYKodqpqlqpXRc/9FRL7bh0PeADyjqtmq+gdV/Zyq/qwvMcawya4m6VhyM/0m+maerarZwEbg7Jiy\nv3Szu9D7N1npovxB4JMikrJX+WeAp1S1qZfniQejgeVuB2FMorDkZgaKsFfyEZE0EfmNiFSIyCYR\nuVNEvCKSDzwFjItp6eWJyLEiskBEakVki4j8QkS6/Z1V1XlAPXBezLl9wEVEEh8i4hGR74nIWhHZ\nJiIPi0h2pxciMlJEnhWRahFZKSKXxWzzisjN0ePUichbIlIU220qIlcDnwC+F722v4rITSLyyF7n\nuV9Ebuvk/K8Ds4AHovuP2LslKCIfi3Zb1orIKyJySBfXEhCRR6P1FgPTu/t5GpOILLmZwfQD4FBg\nCnAEMBu4TlVrgI8B62JaerVAB3CVquYBJwDnAF/o4bkeAS6LeX020Aa8FH39beBU4FhgBBAE7u7i\nWH8DVgDFwCXAXSJyTHTbd4kk0VNVNRf4YvQ8EG2JquqvgL8DP4xe24XAw8C5IpIOkXt0wCeJJt9Y\nqnoc8C7w+ej+W2K3i8gs4NfAZ4H86LH/2cUHgZ8ARcCoaNyXd3HNxiQ0S25mMH0a+L6q1qrqduBH\nwKVdVVbVd1V1YfT79cADwEk9PNdDwOkiUhh9fSnwiO5eTPVLwHdUtUpVO4AfAhfufRARmQAcBtyo\nqqFoPA/GxH0FcH00PlR1sao27Nx9P9e2kUjC+ni06DwiyX3lfq6pq+N9Efi1qr6vEX8AUol8gNjb\nBcAPVLUxGsNv9nM+YxKWJTczmEqATTGvNwLDu6osIoeIyHMiUiki9cD3gMKu6sdS1TVEksenRSSX\nSMvtoZgqI4HnRKRGRGqA96LnzN/rUKXAdlVt7yLu4cC6nsTUiYeI3AeESIvw4QM8zmjguzuvRURq\nifyc9vjZiogQ+T+IbfltPMBzGhPXLLmZwbSVyBvxTqOB8uj3nQ0m+T2wEBirqjlEWlddtoY68SCR\nrrpPAUtVdUXMti3AR1Q1P/qVp6oZ0S7SWBXAMBFJjSkbFRN3OTC+B7F0dn1PArNEZDJwOvBYD47T\nmc1EWsSx15Kpqv/cI4BIq7WKSGLfKfb/w5ikYcnNDKbHgZtFJF9Eiojcr9rZWqkCikQkI6Z+JlCv\nqq0iMgX4v16e7wngECLD6Pe+l3UfcIeIjACIDgI5J2a7wK4W4FLgR9G5azOI3MvbGfcfgJ+IyNjo\ncaZ1MTClChgXW6CqzcAzwF+A/0W7ag/E/cDVInJENIZMETlXRNI6qfsEcKOIZIvIaODKAzynMXHN\nkpsZKJ21VL5PZDj7B0S6AV8FfgqRe1XAXGBjtGstF/gm8H8i0gD8ikhy7O4cuzeq1gNPE+mK23sq\nwh3Af4GXo12er7HnyMHYY19AZBBMZfQ431LVN6PbbgeejTnOvUTud+19jPuBmdFri22hPQhMZc8u\n004vp6vXqvoG8DXgvmiX5Erg4pg6sfveBFQT6R5+hk4GsBiTDGSwH1YqIg8QGfVWpaqHdVHnHmAO\n0AxcrqrvD2KIxgya6ICVd4Dive7rGWP6wI2W25+AM7raKCJzgPGqOoHIiLbfDVZgxgwmEfEC1xIZ\nxWmJzZh+5BvsE6rqa9G+/q58lGgXjaq+JSI5IlKsqlWDE6ExA09E8oh0Da4BznQ5HGOSzqAntx4Y\nTmT0107l0TJLbiZpRCepZ7kdhzHJygaUGGOMSTrx2HIrZ895OCPYPadoDyJiq5kbY5KaqvZmbmeX\nxvg8ujHc57fMjao6ph/CGXBuJbd9FtWNMRe4CvhrdM28uv3dbyu7+QMa5/+OWcU/owdr6vYLVYdw\nqJlQsJFwqJFwqIlQsIlwqIlwuIVwqJlwqIVwqAUn3Iri0PXlaifbdv4C7lku4sXjSUHER3Xliwwb\nfjbi8SHiRSTm311lseVeEC+jl6XgwYsQ3Y4n8j0S+V48gAeJ/hftLot+H7MNBJHYujHlu2KXaOx7\n/pfLPte8V/29Xu/0TsNPOCp77yfHaMx32smxOztPT+3vzaDrY3Udw4G/T73T8GOOyr4xpuTAYov8\nlHSPf/co06627T7nnnWdaA0n+ruuqIajr8M4GkaJvo753iHMpqlBVKPlGobov5GvEOqEqdz0JEUj\nzkGdII4T3P2vRv6NXK3ExEWXZfv+3GRXmceTitebjtcXwOvLwOvLwOMN7Hrt9+fiS8nB58tGPN79\n/Hz7rqNtO+/uuIWmV+/vt2NuDCs6otO1wXtMtjQkzKT/QU9u0Tk+s4ECEdkE3AykEFlA4X5VfU5E\nzhKRNUSmAnxuf8drePkeZpX+4oATm6oS6qilvW0r7a1VdLRV0t5aSSjUuL+rwOvLwOfLxOfPwuvL\nwufPYuLKMnySgV8CMf+mD0jSXdDczKyqr/V+x9Tuq8Qzr6SQ4unJ49iSj0f8+D0Bt8PoVyNWdV9n\nQcMaZpX3dL3sA6OqhOkgpC2EtIWg0xz5V5tZN6WBjrZtNAbrCXXUEwo2RJN4ZwR/Si4pqYWkpBWT\nnjGatMBIPN7e/eGlpA0j+5Rv9GtyG2rcGC356R7U+WpPjzez+DY83r0f27V/tdtfZ0fF84jHx85f\nxtS0ElLSS5m2bhYBbwkpkhVtbfRC78IwxsQJEcFHKj5JBfIgpmFWtqbnx1F1aNc6WsM7WHFoOXU7\nFtDW8jccpyN6Hi/jptzYo/eWQz88iIpeXofZLR7vufWKz9/7AWc1VfM4u/6HeKSTy/f3Q1CDYETq\nbLdDcMVQvW4YuteeSNct4iFN8knz5HPshxP32b7g4PlUbnyc0jH7fTC96QdDcrSkaqjzxJZARqTN\ndjsEVwzV64ahe+3JdN2zVp5IY91SOtqr3Q4l6Q3J5MYgLzlmjDE7nVF/E2uX3kIouL/7+qavhmZy\nM8YYl6R6sjmz6YesXnwj4XBb9zuYAzLkkluwoxZfSo7bYRhjhrB0byFjJ1/H6ve/i+OE3A4nKQ25\n5NbSuJZAZk+eLWmMMQNn2sIxjJx4JWsW34RqV1MLzIEacsmtqf4Dpq6a4nYYxhjDEe8fQumYi1n1\n/nfsHlw/G1LJTVVpqltGnn+S26EYYwwARy07gtPqr2ft0lup27HA7XCSxpBKbvU73iR32DFuh2GM\nMXsIeIs5r+kudlQ873YoSWNIJbdtW+ZywvpPuB2GMcbsQ0Sgt6simS4NmeTW0rSOtIyReGRgFzw1\nxhjjviGT3CrWP8LsrZ93OwxjjDGDYEgkt2BHHSJeUjz24GNjTPzy+bJs1GQ/GRLJrXztHzlxu7Xa\njDHxLTW9jPbWTp/NbHop6ZNbS+NaHKeDLN/I7isbY4yLUlILbVHlfpLUya2jvZqNH97DGdXXuR2K\nMcZ066DlhYQ66twOIykkbXILh1pZu/QWzmq+Da/YU0SNMfEvzZNPsKPG7TCSQlImN3XCrF5yE2Mn\nf4dUT7bb4RhjTI+kefIJtidWchORESLysoh8ICJLReRre22/VkQcEcmPKbtBRFaLyAoROT2mfIaI\nLBGRVSJyd0x5iog8Ht3nTREZ1V1cSZnc1n3wE4aPu5xpC0e7HYoxxvRYimQTDNa7HUZvhYBvquoU\n4BjgKhE5GCKJDzgN2LizsogcAnwKOASYA/xWZNfs9XuBK1R1IjBRRM6Ill8B1KjqBOBu4M7ugkq6\n5LZp9b3kDjueo5ZOdzsUY4zpFREPkFgPU1bVSlV9P/p9E7ACGB7dfBfw7b12+SjwuKqGVHUDsBqY\nKSIlQJaqvhOt9xBwfsw+D0a/fxI4pbu4kiq5VW56Er8/l+NWn+Z2KMYYM+SIyBhgGvCWiJwHbFbV\npXtVGw5sjnldHi0bDmyJKd/C7iS5ax9VDQN1sd2cnfEd2CXEn+3lzxHqqOXUrVe6HYoxxvRBYq4v\nKSKZRFpV1wBh4LtEuiQH5HTdVUia5FZf/Tbn1P/I7TCMMaaPBrBb8oTejUOYV9XEvG3Nuwu2NHRa\nT0R8RBLbw6r6tIgcCowBFkfvp40A3hORmURaarEDQkZEy8qBkZ2UE7OtQkS8QLaq7nfkTdIkNySp\neliNMcZ1s4szmV2cuev1rcu2dVX1j8ByVf0lgKouA0p2bhSR9cAMVa0VkbnAoyLyCyLdjQcBb6uq\nikh9NAG+A1wG3BM9xFzgs8BbwAXAy93FnjzJzRhjkkJidUuKyHHAJcBSEVlEpOn5XVWNfTidEr0w\nVV0uIk8Ay4Eg8BVV3dlcvQr4M5AGPBdzjAeAh0VkNVANXNRdXJbcjDEmriTcaMnXgf0+S0xVx+31\n+jbgtk7qLQSmdlLeTmT6QI9ZX54xxpikk0TJLbE+7RhjTGdEvKgTdjuMhJcUyS3SXZtY/dTGGNMZ\njzedcLjV7TASXnIkN6cDj8cWRzbGJD6vN51wuMXtMBJeUiS3cLgFrzfd7TCMMabPvN50HGu59Vly\nJLdQM15fhtthGGNMn3ksufULS27GGBNHvL4A4ZB1S/aVJTdjjIkjNqCkfyRHcgu34PUG3A7DGGP6\nbOwHmTjhNrfDSHhJkdyccDtjP8jsvqIxxsQ5n6RbcusHSZLc2vBKmtthGGNMn/kkjbAltz5LiuQW\nDrfhs+RmjEkCXkmzlls/SIrk5oRbreVmjEkKXtJwnHa3w0h4SZLc2vBiyc0Yk/h81nLrF8mR3Jw2\nfGIrlBhjEl+kW9Jabn2VFMktHG7HK6luh2GMMX3mlVQcx1pufZUUyS0yWtKSmzEm8XnwoU7I7TAS\nXlIkN9UQHnuouDEmCYjY47v6Q1IkN7BfCGNM8lB7+HKfJU1yM8YYY3ZKkuRmrTZjjDG7JUlyM8YY\nY3ZLklEY1j9tjEkmA/SeNmt03/b/y9L+iWMQWMvNGGPijt1q6StLbsYYY5JOkiQ3+5RjjDFmtyRJ\nbsYYY8xultyMMcYkHUtuxhhjkk6SJDebCmCMSSb2ntZXSZLcbECJMSaZ2HtaXyVJcjPGGGN2cyW5\niciZIrJSRFaJyPWdbM8Wkbki8r6ILBWRy10I0xhjTIIa9OQmIh7g18AZwBTgYhE5eK9qVwEfqOo0\n4GTg5yKSJEuFGWOMGWhutNxmAqtVdaOqBoHHgY/uVUeBrOj3WUC1qtqjaY0xxvSIG8ltOLA55vWW\naFmsXwOTRaQCWAxcM0ixGWOMSQLxOqDkDGCRqpYB04HfiEhm19Vt2KwxJpnYe1pfuZHcyoFRMa9H\nRMtifQ54CkBV1wLrgb3vywGwZukPqKmaz4L6W9jSNq//ozXGmEGypW0eC+pvoaZqPmuW/sDtcBKa\nG4M03gEOEpHRwFbgIuDivepsBE4FXheRYmAisK6zg42fciMiHmbV3jJwERtjzCAYkTabEWmz2ZHr\ncNDU77Nu2Q/dDilhDXpyU9WwiHwVeIFIy/EBVV0hIl+KbNb7gR8BfxaRJdHdrlPVms6O52gQEf+g\nxG6MMSYxuDK8XlWfBybtVXZfzPdbidx36/5YThCPx5KbMcaY3eJ1QEmPqRNEPDYFzhhjzG4Jn9wc\nJ4hYy80YY1whIiNE5GUR+SC6otTXouV5IvKCiHwoIv8RkZyYfW4QkdUiskJETo8pnyEiS6KrV90d\nU54iIo9H93lTREbRjYRPbqpBPHbPzRiTVBJqKkAI+KaqTgGOAa6Krjr1HeBFVZ0EvAzcACAik4FP\nAYcAc4DfisjOlaLvBa5Q1YnARBHZeXvqCqBGVScAdwN3dhdUwie3SMstxe0wjDGmHyXOUwFUtVJV\n349+3wSsIDLF66PAg9FqDwLnR78/D3hcVUOqugFYDcwUkRIgS1XfidZ7KGaf2GM9CZzSXVwJn9zU\n6bABJcYYEwdEZAwwDVgAFKtqFUQSIFAUrbb3KlXl0bLhRFas2il29apd+6hqGKgTkfz9xZLwyc1x\nOqzlZowxLouuIvUkcE20Bbd332p/9rV227RN+GGGkakAltyMMclF1f37bvNW72Demupu60Wf2vIk\n8LCqPh0trhKRYlWtinY5bouWlwMjY3bfuUpVV+Wx+1SIiBfI7mru804Jn9wiLTfrljTGJA8RL6jT\n78dtnTqiV/WPnjqCo2Ne3/r86q6q/hFYrqq/jCmbC1wO3AF8Fng6pvxREbmLSHfjQcDbqqoiUi8i\nM4msZHUZcE/MPp8F3gIuIDJAZb8SPrmpE7KWmzEmqYh4UPo/uQ0EETkOuARYKiKLiHQ/fpdIUntC\nRD5PZEnFTwGo6nIReQJYDgSBr+juZupVwJ+BNOC56IIfAA8AD4vIaqCayLKN+5Xwyc1xgoxelgqp\nbkdijDH9xUNk3ET8U9XXAW8Xm0/tYp/bgNs6KV8ITO2kvJ1ocuyphB9QohrCi3VLGmOSh4gH4uCe\nWyJLiuQWuZdpjDFJQhKn5RavEj+5OSE8id+7aowxu4h4IEHuucWrhM8KqpbcjPtUlQ5tpDVcRYc2\nIPjwiA8PPiT6r1dSSPMU4rXl4kw3BA86AKMlh5KEzwrWLWkGS1iD1ASXs3jMW7Q1bwLZs+PD58si\nJa0Inz8b1TCqIdQJRf8N4jgddLTvQJ1gZAcRQEhNKyaQNZGjPjyGFE/m4F+YiT/WLdlnCZ8VrFvS\nDJTmcCVvjf43LY1rABDxkZEzmYL8j5CWMZrda70eOFWHjrYqmupX8tKwewiFmhCEjJzJHLnuRLJ8\nI7s/iEk6AzXPbShJ+KwQabl1NQrVmJ5TdXjn0HeornyJULCBlLQiCgvOoHTMJdF7IP1PxENqeimp\n6aUUlJy8K46m+uUsGPkMbS2RpfbyCo/jmDWn4RWb0zkUiLXc+izxk5u13EwfqDq8Nv5f1FUvACC7\neRojJ3wZnz/btZhEPGTlHkpW7qGRGJ0wtTve4N95P0KdDrLypnPs+rOtCzOpCQn22Ju4k/BZQTWE\nx+65mV4KOs3MK/sTrU0bKPTN4aCptwxY66yvxOMlv+gE8otOQFVprF3Ei4V3Ewo2UDzyYxy9/Bi3\nQzT9LNJys+TWFwmfFVTDSJeT443ZU1O4nPmFD+BokLKSSwhkHeR2SL0iImTnzyA7fwaOE6Jq8995\nOvNvFBSfwnFrzuqX+4AmHgg2FaBvkiK5Wbek6c5701dRvuYB/GmFjBx3Jf6UPLdD6jOPx0fp6Asp\nGfUpaqpeZm7WtWTlHsZJmy+x6QaJzlYo6bOEzwrq2IAS07WwBnmx6Jc4m0KMn/p9PN7kW4RURCgo\nOYWCklNoqFnEs3U3kJV3OLM3fcZacgkqkRZOjleJn9zUsZab6dSCg+dTuekJRpZdSUb2JLfDGRTZ\n+dPJzp9O7fbXeDrjaoaP+xxHLTvC7bBMr4lN4u6j+LyD3guqIbvnZvbQ5tTwTPYNtDStZdKMu4ZM\nYouVN+x4Jk7/GXU7FvBs7s10OE1uh2R6xUZL9lXCN3nUCeKx+wsmat7ov9BYt5Qxk76BPzXf7XBc\n5fH4GDnhS7S1VPDv0PfJG3YCJ67/mNthmR4QEbvn1kcJ33JzbG1JQ2Rtx38X3IbHm86Ew34w5BNb\nrLRAGZOm34l4fDydeS1Bp9ntkEy37F5pXyV8cgPspvkQF9I25mZdS0HJKRQNP8ftcOLWsLI5jDn4\n6zwTuJb3pq92OxzTLWu59UUSJDf7BRjKWsLbeSbwDUZPvJrs/BluhxP3UtNLOXjGzylf+wCvT3jB\n7XBMl+wDe18lQXIzQ9XCwz/ghaxbmXD4T0jLsAWGe8rjTWXC4T+itXkDL5T8ylbCiFNqH9z7xJKb\nSUhvTHqZrRseY+L0n+HzZ7kdTkIaMf7zZOVO5ZmsbxPSVrfDMTHsVkvfWXIzCef1CS/QWLOI8VNv\nweOxwUR9kTfseEZN+irPBL5Ju9PgdjgmlrWo+yQJkpt9whlKFh6+jNrtrzL64Gvs020/SQuMYPzU\nW3gu4zvWgosbNs+tr5IguZmhojW8g82rf8e4KTe6HUrSSUktYOzk7/CvjOsIa9DtcIwltz6zPh2T\nEELazvOZ32fCYT+yrsgBkhYoY9TEr/KvNddzXuPP4vYRQEOCyIAM9Plw/Ih+P2a8st9eE/dUleey\nbmTMId9y9SGiQ0EgazxlYy/j2dzv2ShKF4m13PrMkpuJe/8p+hnFoz5OesYot0MZErJyD6WwbA7/\nLviJ26EMXWLJra8suZm4Nm/046QFRpFTMNPtUIaU3MJZ5BQcyQvFd7sdyhBlg6X6KgmSm326SVbv\nHraE1qZ1lIz6hNuhDEkFJaeQklbM/LFPuR3KkGTdwn2TBMnNPuEkI1WHLWvuZ/TB33Q7lCGtZNQF\nNNQspClc4XYoQ4x1S/ZVEiQ3k4xeGv4HSsd8xkZGxoGxk6/nxazb7OGZg8geedN3ltxM3GkNV9Pa\nvIGcgiPdDsUAXl+A0jGf5sXSe90OZQixlltfWXIzcefF/J8zZtLX3Q7DxMgpOIpwqIV3D1vidihD\nhNjCyX1kyc3ElbcmLyA9Y4w9bDQOjZp0NZtX/46wdrgdStKzbsm+s+Rm4oaqw9YNjzB83OVuh2I6\n4fH4GD3pGv5TcKfboQwBNlCur5Igudmnm2Tx6rh/UDTyE7bsUxwLZI3Hl5LDu4ctdjuUIcDe2/oi\nCd5F7BNOsqjd/jp5w453OwzTjRHjrqB87Z/dDmMIsPe2vkiC5GaSwduHvktW7uH2GJsE4PGmkFN4\nNG9M+p/boRjTpSRIbtZ0TwZVm56kZNQFbodheqh45CfYtvkpW0XDxK0kSG4m0bWEq/D5c/F4U9wO\nxfSQiFA04qO8Ou4fbodiTKeSILlZN1aie6Xoj5SNu8ztMEwv5RfPpmbbK7ZyiYlLSZDcTCILaTuh\nYD2paUVuh2IOQMnIT/DK6MfdDsOYfSRBcrM+/0T2xrhnGTb8bLfDMAcod9ix1NcsdDsMY/aRBMnN\nuiUTWX312+QUHO12GKYPsvNn8PaUt9wOw5g9JEFyM4kqpO2Ix2+TthNc8YjzqdrytNthGJeIyAMi\nUiUiS/Yqv1pEVojIUhG5Pab8BhFZHd12ekz5DBFZIiKrROTumPIUEXk8us+bIjKqJ3HZu4pxzZsH\nvUBB8Sluh2H6yONNxeNJpd1pcDsU444/AWfEFojIbOBcYKqqTgV+Fi0/BPgUcAgwB/it7J7cei9w\nhapOBCaKyM5jXgHUqOoE4G6gR+u/WXIzrqnb/ga5hce4HYbpB6WjL+TV4Y+6HYZxgaq+BtTuVXwl\ncLuqhqJ1dkTLPwo8rqohVd0ArAZmikgJkKWq70TrPQScH7PPg9HvnwR69InYkptxRViDiHgQj9ft\nUEw/CGQdREvjWpvUbXaaCJwoIgtE5H8ickS0fDiwOaZeebRsOLAlpnxLtGyPfVQ1DNSJSLePDbHk\nZlyxYMJL5BWd5HYYph9lFxzB21MWuB2GiQ8+IE9VZwHXAX/rx2P3aBShrx9P2GMiciaRvlMP8ICq\n3tFJndnAXYAf2K6qJ3d6LBstmZBqd7zBuCk3uB2G6UdFw89j/fI7Aetqjlcrikt7VX/5KytZPv/D\nAznVZuApAFV9R0TCIlJApKUWOyBkRLSsHBjZSTkx2ypExAtkq2pNdwEMenKTyNC4XxPpN60A3hGR\np1V1ZUydHOA3wOmqWi4ihV0ez+NKfjZ9pBrC4/G7HYbpRx5vKo4G3Q7D9KPJJx3M5JMO3vX6qR/P\n7aqqsGeL6p/AR4BXRGQikKKq1SIyF3hURH5BpLvxIOBtVVURqReRmcA7wGXAPdFjzQU+C7wFXAC8\n3JPY3cgMM4HVqroRQEQeJ3LDcGVMnU8Df1fVctjjZuQ+RCy5JRpHw0Q+gJlkk5pWQlNNBZneMrdD\nMYNERB4DZgMFIrIJuBn4I/AnEVkKtBNJVqjqchF5AlgOBIGv6O4btVcBfwbSgOdU9flo+QPAwyKy\nGqgGLupJXG5khr1vKG4hkvBiTQT8IvI/IBO4R1Uf7uxg9iaZeGpDK8jMPsTtMHpNnTBtrVv4cNQq\nQrWb8ATymFh+MOkZY/H60t0OLy4UlJ7GW75/c0r5FW6HYgaJqn66i02XdlH/NuC2TsoXAlM7KW8n\nMn2gV+K12eMDZhBp1mYAb4rIm6q6Zu+KNgE48bw/+k3yCma7HcZ+qSqL85+nbc1rIB5AEfHizS3D\nx1hSxx2D01zLh8MWEar+Bxps27WvN6eU6c6l+PxZ7l2ASwKZB1Gx7iG3wzDGleTW1Q3FWFuAHara\nBrSJyHzgcGCf5Laj8kUWNDVFDpQ6mxFpswciZtOPWps3UJYxxu0wOuWE23nP/wjBig9Iy5lNzpwb\nEU8XH6AKIXX0EfsUB3esY+E7P0NRMo68kKlrpwxw1PFDREAER8N4rFel17a0zWNL+zy2blyBz5/t\ndjgJzY3k9g5wkIiMBrYS6T+9eK86TwO/io6MSQWOBn7R2cGGDT+bWZVXD2C4ZiDE2xO3O9q2877z\nR5zmWgIzPkHmzEsO+Fj+wnHkzLkBp72J5nf+ypvVfyRt4slMqztrSPQ0ZOfN4J3hb3P0chs12Vsj\n0iIf0DtGP0VaYCTla//gdkgJa9CTm6qGReSrwAvsngqwQkS+FNms96vqShH5D7AECAP3q+ryzo5n\n99wSi6pGu/niQ7CjnoX1P8GTkU/giE/hy+ndUOn98aRmknX8Fagq7avmsWDrN0mfehbTqk/vfucE\nlld0AlucFnJYAAAgAElEQVQ3PIpNCegrmxDfF67cc4uOgpm0V9l9e73+GdH1yPbHkltiaXW2k5La\n5cyOQbVkxFs0vfUIOWd+B29GwYCdR0RIm3QyqRNn0/zOY7zdfDtHpX87aVdnSUktINje7TQks1/x\n1bORiOLnI/QBsuSWWBrDm0gL9GhR7wGjqryr99G2ej55H7tjQBNbLBEhc+YlpE0+gzcrrqG9deug\nnNeYoSjhk1tSXMIQsnLiBtIyRnZfcYCEQs0s2H4dvsKxZJ98ddeDRQZQSukh5J57K4ua7mZR7nOD\nfv7B4PUFCDrNbodhhrCEzwzWckssbS2bSAu4k9yWjnmftyuvI/ukq0ibcKIrMezkSc0g99xbcZq2\n81bjj3GckKvx9Les3MN5d/K7bodhhrAkSG4JfwlDSrC9Bn9Ktwt697vFxa/QvOipSDdkTsmgn78r\nGUdeSOCwc1iw9Zs4TvIsXZWdP4OGmvfcDsMMYQmfGazllmBEBn0awLIJK2ld9hw5Z96AeONv3QJ/\n8SSyTvoyb227HlXH7XD6RUraMDrat7sdhhnCEj65xdOwchN/VB0a5v0mMhk7zubWxfIXjiMw7WMs\n5Pduh9Jv7INnX9g0gL5K+MxgCycnmsFNMAu5n4wjL0J8KYN63gOROuYowvVbaWvZ0n1lMwTE74ex\nRJDwmcEeeWO60tZSTrijksxjLj+g/cNNO6iqfhLqqtHQXgM+nDCEQ3gmTaM44xw8KYG+BwxknXw1\ni579IbPS74rrlmZPCB5UHbsvblyR8JnBuj5MZ1SVRfU/Jees7/V+XyfM1i0PoNvK8c35NBQUI/59\nW36qin74PpVv/xxtb8Uz8XCKM8/tU6LzpGaQfugc3mt6lCM6PnPAx4kHKWnDaHG2keGNnwE8icO6\nJfsqCZJbwl/CEDM4f7SL0h4nffIZeFIzerVfpe81wi8+ifeMi/Ccvf/kIiLIwdPxHDw9kuhWLaby\n7cgSqKWHfhvxpR5Q7GkTT6LumVvoyN4RN6u5HIjU9DKawxWW3A5Ugrfc3Zbw/QXWcjN7C7bX0LFl\nMWmTTu7xPuGmHZS/fRPOxlX4rvoRnomH9eqcIoJn0jR8l34Tz6mfoGL+t6kqXt3b0HfJPuXrLKy7\n/YD3jwep6WWsPHiT22GYISrhk9vopfE/UMDEGvhPowsb7iTrI1/rcf2t1X+hcuWv8F1wJb4zL+rz\nqiWe0tH4rryF0Nw/szX4fPc7dHaMQC6p42axKOuffYrFTWmB4bS37P00K9MTux9ObQ5Uwic3j3VL\nmhjtrVV4MgvwZvRsovjW4PNoSxP+z12PZOf1WxySkob/Czei61awNfTCAR0jMPUc2te+0W8xDTaf\nP4dQsMHtMBKW2GjJPkn4zCBYt6TZbYnvCQJTzu9R3aqiVThz5+P/v5s63T6qcT71//kv4vfvc/9D\ng5HRk3UnfhkZ1vVjcnyf/BKhR+6i8sgMSoLH9fAqdvPmDqetebOr63EeKBEvqmG3wzBDVOInN7vn\nZmKEajfhy+8+EWiwjdDjv8Z35S2dbi96/8801zdQet21XQ7JDzc24X3kETQUpubEK5HMnE7reS/5\nOqEHfkLlqQFKGqf3+FoAAjM+zpK3H2Mm1/dqv3gg4rPkZlyT+N2SiZ+fTT9paVqHr2Bsj+pWLPoR\nvguvQlLS9ijXUJDcf/4Af2EhRZ//7H7nmnmzMim+8osUXHQBeS/+koL5v0I72vapJyL4Pvcdwv96\nmHDTjl5dkzdzGE5TdWLegxGPJTfjmoTPDNYtaXZaJn8lY9pl3darKP8jnmnHISV7tvC0ZhtZc28n\n/wufI3X0KNRxaF2+kjI201HXSEdNPR11jYRb29Gww5hL5lCRMw3/sEJKr72G9g0bCf7lR2TOmknF\nqPP2OLZ4vfgu/zaVD9/B8ON/2qvrShlzFEuyX+XwSnefZNBbiT4J3SS2hE9uHuuWTCgDdZNcVXGa\na7t98GhlyhvQ3ID3zIv2KB/V9Cq1zz9L6Xe+hTcjg3BTE+2/u5PCo6fiKSkg55Cx+HOzSMnLwpee\nhhMMsfKev5CS8x7Bcz6HiJA6ZjTDb/g22x96lOG+5ygvO2uPc0hmDp4jZ7O1+nFKC/Y8//6kTz6D\n+v/cAQWJldxM/FmdWux2CIMm4bslreWWWHSAJnEvHfEW/pHT9lsn3FxD+KWn8H78//YoL/3wCZoX\nLmL4Td/Bm5HBiNYPaP3VT5h83eWM+uSpFB0/nZzJ4wiUDcOXHunG9Ph9TL72UnIPm0DLT79HcEf1\nruMNu+wSWpd9wKiG+fvE4D3qZJy1ywg39nzFfPGlgMdLOLxvl2f8S8DuVJMUEr/llviXYPpB+8Z3\nCUw9Z791qmqfwvvRz+/RXab1NbSuWkXp168GwGlrZ+XdjzL9zq+Tufh1Vv7yLVIy9l1ppLWmiUnn\nzqTwxNPJmTyOxTf+DLnqBnw52QAUffn/KP/hbeinT9ine853/hVUvfIXyrJ6PhcvpWwKLRUfkpV3\neI/3cZs6YSTxPz+bBJXwv3k2WjKxeL3phEMt/X7ccOM2PFlF+62jFRuRstF7lOW9ei/DLrtk1+vQ\nn+/ikGsvY9jmJaz/07PkhZvJD+37lRtuYcsTL5Lx3nz8mQEOu+XLtP3mNsItkWsTEbKOncXIbfvO\ncZOCYrR2W6+uz5s3ktUj1/ZqH7cFg3X4UnLdDsMMUYmf3BL/EoaUtIxRtDZv7P8Dq/ZoZZHYVtT4\n1OV40tLx5UcmfGe88XcKjpzMhPQalt35GNmNTRS2NjOsuXGfr/yWZnKam1n7+7nkfvg2KXnZTPnu\nFbTe82Ocjg4Asj8ym4aX53UeR2o6Tntzjy/Plz+ScG1iPQon2F6d0GtjmsSW8JnB7rkllvSMUbQ1\nD8R6g/u/txOq3ogUD9+jbMejf6Hw0osBGNG8jPrl6xh2/HRevvlRwsEwGfVNDGtopqCmYZ+v3Pom\ncuobyWpuYsVdf2XY5sWkFxdwyNcvIfznuwEQj4f0yQczuvXNfeLxTD+equB/e3x1nozCXk8jcFuw\nfQd+S27GJYmf3KxbMqEcumw8rc0bBv282+qewXv0abtejwm+TcrIEXgDAZz2Dlbe8xgTv3YxG7//\nUzJaG8lubUEdJXV7I76qhl1f/m2Rr5TqZqS6mZz6FjIbm1j8o4co27GCjNGlpJcNo6giktDyzj2b\n2rn/2iceOXgGzor3ehy/iECCzXXraK9m4vKhMzrPxJeET24ea7kllAxPCe1tVf16TMcJdfvQWq2u\n3GOZrOq/PUXBBZ+IbHv8N0y6+mIqbvsV6c315LS1kd3Ygre2mYyqRjzl9Uj0iy2RL6lsQKua8NQ0\nkdPYQjgU5uXvPUJ7dT3jLj+X9Q8/izoO4veTMryMcd4le8QjXi+ok5iTs3so2L6DdI+13Iw7Ej65\nWbdkYhHxgDr9esz21gq8OV0/M8xpb0LSdz/XbWTNy2RMPxzx+xnZvgJvagptc+eS0VRLkROiqK2d\nQGMrvupm0jbWkbO5Hu+menRjHWysQzfW4alogKqm6JqTSm57K5nBZjbc8nOcjiBjLjmL1P8+CkD+\nBR+n+sl/7BOXZ9REQlUf9uvPIp50tFeT5tn/vENjBkqPx9GLyBvAvcATqto+cCH1jnVLJh6lf5Ob\nx5OChkNdn6+jFdIzd71uW7OW7JNOAKB5QwV5h08k9OZr5BdmUZAuFPgcPMEgnqZ2FlQ18+vcNHI9\ngj9mMMpr7SHeaA8jJZm0l+SwPT+HbZlZ7PBm0r6tloIjDqHiudfwA95AAPF0Mnk9bxhOeW2//Rzi\nTTjUiN/Tu4fFGtNfetNy6wAeBCpE5BcicvAAxdQrNloy8fj9uQQ76vrteClpRYQbux5a78koQOtr\ndr32ZmUSbmoCoFxH0FG772NZfF7BI8L3s1O5q6ljn0+BjY4iY/Mit8F29iyqoqqI34c6TvdPUm6s\nxRPov8fsxB9bfsu4p8eZQVVnA5OJJLjLgA9EZJ6IXCgi/gGKr1vWLZl4cgpmUl/9Tr8dL9LV2fW9\nq8gUgd3bvVlZhBsaI9/n5tBeXd/lvlM+Mo6Ppfv5cWPHHuVN0cMpkaW/1FEcR0EVb4qfUFMLvsz0\n2CD3ObY21OIJ9Oy5c4lGtQfJ3ZgB1Ktmj6quVNVvAsOBywEv8BiwRURuF5Fx/R/i/lnLLfEcuWIm\nDTULXTv/5oYSwo2R5ObLzd2j5Sayb3vjqFPGcVyKl7sbd/fGN6nioDga+dJIXsNxFE+Kj476ZlKy\nd3eFdpp8G2rxBHo2yblHLcE40tq8gfSMMW6HYYawA8oMqtquqg8D1wCvAsOA64BVIvI3Een67n4/\nE7Hklmj8ngBOuNW9ADKycaLJzZOWFlnlf6/cs/frk08bzzifhweaIy24JoWwQtgBx4kkNY223Dwp\nfoINTfhzMtkfDYcj60b2gNO0HW/msJ5dXxxoqlvK1PX7X+vTmIHU68wgIuki8nkReRt4BygikuTK\ngCuBY4FH+zXK/cVj3ZIJSiJdV/11NH8qGuzZwsISyCLc2NRtvb3bWuedfhDpIvy1JUiTo4QcJew4\nhMMO4XAYx3Fwwg7i9xGsb8KfHRlMoY4Dna6e0vNpAKHaLfjyRvS4vtua6leQ75/sdhhmCOtxchOR\nqSLya6AC+B2wEThVVSer6q9UtVJVfw98GThuYMLtJC7rlkxIgawJtDSu6rfj+fJGEqrpelkv8frQ\njmi3YmY2obrdA1o07OCEwoSCYYLBMB3BMB1hh6Czb/L59BkHUeco/2gN0hp2aAs6tIccQkGHUHuI\ncHsQj9dLR20DFeEyAJzmFjxp6fscC6fnyT1cu4WDNg96r/8Bc8Kt+KSTazZmkPRmSf3FRBLb3cD9\nqrq1i3prgH3XGxogltwS06yNc5jf/iAZ2f0z6PbQxtNZvOIx/MWTOt3umXUazry5eE+/AEnPIFxb\nR7ipCW9mJpkHjSTQUE7Lym00tLbhberA1xpEgmG+tWgrGb7I75hPINvvJWtiAR9rC7K5JQRtIUJN\n7TR4WmkJChkHl+EEQ2z97wIyvjUHgO1/fJC8j3+UmpgxKbqtHAlk9fj6Ora8T0b+Jw78BzSIVJ0B\ne7SRMT3Vm8zwSWC0qt66n8SGqq5Q1ZP7HlrP2NN+E1PAW0Swrf/WSkxNLyXcUNnlih8lbUfjrF+x\n63X9Gd9g230PANBx+mfYFsrEO2k8dRkB6nIzCRVkIcVZUJqNjMjBOzKXtLEFOKNyaRyeS3hELhRn\nEizIoDYrg5q0dGT8GPK/8n+svPtRJl51IeL30/jGAlJGDmddx5Q94gk98xAl47/co2tz2hoQfzqe\nblZhiRdNdcvIyjnU7TDMENebqQBPqWp4IIMxQ0sg6yCaG/qvazJt/HG0r329y+2e8ZNx1iwDQPKL\nSB07hqa3I1MS9MKvUNHgxXPQOOoCGdTnZhIqzCRUmEW4JAffyHx0eC7O8HzCw3MJl+bQnpdBTUaA\n6rR0dMwoCr72ZdY99C8yRpdSkX04ofoG6l/6H5WHXbZHHM6WdUh+EZ60nrXcWt77O4HpHz+wH4oL\nara9wsz1p7gdhhnirE/PuOa4LZ9k25an++140+rPpW1F1yvtl+R/ivD83YsYVx52KXX/fgGnrQ0R\nwfv5a9lcFULHjqY2EKA2O5OGYTk0Dy+gtiSP6uI8thflsa0oj+rCXGozA9SmB9BRIxj2jStZfsef\nSSvOp/WkCwGo+u19NJx93T69C+HnHqV09J5PA9+f4I51TF2XOC2hjvbtBLy2YLJxlyU345o0Tx7B\njtp+WzxYPF4kJdDlc9LEn4b4/GhLZKSkiFD0hc+x7fd/2vU69Ss3sLminfDw4dSkB2jIz6M6L5/t\nuXlsy82lKieHquwctmVmUxcI4Iwoo/CrX+T9G35F2dnH0zDtbABqn/03WcccjeTsOUnbWfsBMmIc\n4k/r0TV1lC8jpSxxEps9fXvoEZEHRKRKRJbElN0pIitE5H0R+buIZMdsu0FEVke3nx5TPkNElojI\nKhG5O6Y8RUQej+7zpoiM6klc9ltoXJWVO5Wm+mX9dry0g0+h7cOXu9zuOfl8wi/vXsR4bdshpIwa\nSd1z/wEiq/WnffVGNle001FYRF16DjvSctiRmsN2fw7b/dls80W6E0MlpeR+7lIWf/dX+D9zFVVF\nMwHoqKykbeUqykeeu8/5wy/8jdKyy3t8Pa1LnmFa26d6XN9tjXXvk5V3uNthmMH1J+CMvcpeAKao\n6jRgNXADgIhMBj4FHALMAX4ru7s27gWuUNWJwEQR2XnMK4AaVZ1AZEDjnT0JypKbcdVxG89ne/mz\n/Xa8wyqOpWND10t7ldRNRTev3WOh5apDLyFYXU39S/8DwJOaQvrXbqRie5DwtKMITT+a0BHH0HHo\nEbRNOIyCg0rxH3kY6WedycpfPEz617+Hv7gIAKetjarf3k/tmd/a59zOkgV4Jh2OeHu2Wp06YTTU\nhteXOEPqa6peYebaj7gdhhlEqvoaULtX2Yu6eyLrAmDnJM3zgMdVNaSqG4gkvpnRhT+yVHXnH+9D\nwPnR7z9KZNlHgCeBHt3QteRmXJXiySIUrO+3Cd0iHnzDxtO+4e0u63jPuZTQA7dFJldH7Tj2SsIN\njVT+9j40FMIbCBC45ibat9Wwbf57lM99ha3/eZOqee+yaP4mFr60ltr3V5H+zVvwBgIANLzyKhV3\n/oLiL30BSQvscU5n2duE351HScHFPb6Wptd+T+CIxGm1AXS0bSPda4+5MXv4PPBc9PvhwOaYbeXR\nsuHAlpjyLdGyPfaJDmqsE5FuF2VNjLHFJqkVFJ9CdeVLFJae1n3lHjiCL/Lmwmvwlx2KJyWwz/aS\nuqlUnnw+4Yd/gfeya3cN+Kg67FLGTn2PLbf+mOKrvkRKSQltH7kYAfZua/mBEJF1KINVVWz7/Z/I\nOOoIGj59O41tew0gef15tGIDZUfe2uOpK8GqVWiog8M2HdHr63dLQ80isvKmQfeLv5ghQkRuBIKq\n+pf+PGxPKllyM647dvXpPJN9fb8lNxEh+5RraHzpl+TMuaHTOiVtR1M5o4XwE/fiu/Aru8rXywz0\n4kPwPnQ7mTOPJHv2iV2eR0Mhdjz2V8K1ddSffxMN6Rn7/NWFnn8cEaHs4G/2OH51wjS+ej+zyu7q\n8T7xYNuWpzmz7iZ70k0SWT9vCRvmLT2gfUXkcuAsILafuhwYGfN6RLSsq/LYfSok8gDPbFWtoRuW\n3IzrRDykphXT1lJBWqCsX4556MrxLCwcS9vqV0mbcEKndUo4ma1jmgjNfRDfeZ/dHU9qOvWfuJX0\nDx6l4o6fIX4/iCBeH96cbLw5OYjfR9Nb71B48afYkHL0Pu/nqkr4b79DRoyjNOtjvYq96bU/kHnM\nZ/Fsdu1JUr0WDrWCCD7p2ShQ446V7b1cfPuYUyg9JuYW161dNsCEmI81InIm8G3gxL0ebj0XeFRE\n7iLS3XgQ8LaqqojUi8hMImsWXwbcE7PPZ4G3gAuArkeMxbDkZuLCCZWf5RXnAcZO3ncgxoGaEfos\nC5Z+g5SR07qcMF0aOJetpc8RvPdmPNNPwHP0Kbu6DiunXAIxC4tosAOa6tHGemhrRi79FE2dLIis\n4TChB3+Kd+YplOhJvYo5uG0NGmzhsM1H9Wo/t23b8k+KRnwU+u8ZtCZBiMhjwGygQEQ2ATcD3wVS\ngP9G/54WqOpXVHW5iDwBLAeCwFd091ygq4A/A2nAc6r6fLT8AeBhEVkNVAMX9SQuS24mLgS8wwh2\n1OA4oX5bZkpEmJF3A4teuovcs7/fZb3StLPQ4+ZQGXye0O9uwXPQVDwnfxTx7dlyEn8K5A1D8rr+\n9OuUbyD89B/xnnUJJY3TexWvOmEa5/+OWaW/6NV+8aCxbgknb77U7TCMC1T1050U/2k/9W8Dbuuk\nfCEwtZPydiLTB3rFkpuJG4Vlc9hR8WykBdBPUtOLScmdSusHz5M+5cwu64kIpSlz4Pg5VKa+SeiP\ntyPFI/HOuRhJSe1yPw2HcN57DWfpAlAHKRtDyfTv4G0s7HWsjfPvI3PWpXi29OwZb/GipXFt5MGk\njW5Hkkxs4em+suRm4sYxK2fzdMY1FJbOwePtvzf4Ge0X89b2H+JJzyF13DHd1i9pPwaOOYbKnMWE\nH/816uy5pKpk5yN5hTjrV0YeTjrjBMqOuBnxHPizBZve/DPenBIO23L0AR/DLVvWPsCc+ltsYlG/\ns5E5fWHJzcQNEWHkhC+zefVvGX3w1/v12DOzbuLdLfcQrFxJxjGX92hIfkn94TB9z9U2VBWnuZpw\nQxX+6Z/s8ZO0u6KhDhr++3P8Iw9nRlPiLI68U0PNIjKyJuBv3HfKhTFuss9aJq4csXgy4XA7rc2b\n+vW4IsJRKdfgK5pA3TPfx+loOeDjeDMLSSmb0ufEFtyxjtp/3kDGURclZGIDqNjwCCeXf87tMIzZ\nhyU3E3dO2/ENNq369YAce9qOU8k66SvUPX0Twe1rB+QcPdG86ClaFv6NWaV3ceiaQ1yLoy+qt75I\nfvHJeMQ6gEz8seRm4o7fEyBv2PHsqPjPgBz/0BVjmTX8lzS/+1dalj3X/Q79yOlooe5ft+JJyeDo\nnJv79d7iYFJ12F7xHCes7b/BP8b0J0tuJi6dsO5j7Kh8gXC4bUCO7/H4mZX3AzTYRv0LP8Vpqe1+\npz7qKF9G3dzvkXn8F5jemNhJoXLT3ygZ9ckeLydmzGCz/gQTl0SEURO+wqYPf92vE7v3dkT7p2n1\nbGDJ63/Eaa0jdews0qfMQTqZnH0gNNROy/tP01G+BH/RBGaV/RLPysT+swuH22ioWcjJmy5xOxRj\nupTYf2Umqc14fxIvDR9GdeVLFJT06CkXByQ9cwxHcyOaqSxOe5G6uTfhL5pA4MiL8KQc2ONmQjvW\n07zwCTTUQeDwj3JE6LLIhiRo6Kz74CeMnng1vOd2JMZ0zZKbiWunlF/Bv3JuJD1zHIHMsQN6LhFh\nWvVpUHIaS8e8T8MLd4II3qwifIXjIl8Fo/d4Hpuq4rTUENqxnlD1BkLVG3Ba6/Hlj2ZG+tfxp+Ts\n+SCPBFe56W/kFsxi2nsD+39hTF9ZcjNxb07dzcxd+XUmTrsdr29w5lNN3TANCqehqgQ7qlkRWE7H\nxndoWfR3NByM1oo0wzyBPPyFY5m0fQbpGZ/Am5U4DxftjZbGNTQ3rOLs2pvdDsWYbllyM3HPKymc\n3vQ9Xlx6CxOn3TGogxhEhJTUQg6vPBE4EXK6qNhE19uSQDjcxsYP7+G8ll8mRddqYrAluPrCldGS\nInKmiKwUkVUicv1+6h0lIkERScwZrqbfZHhLKR75cTav/o3boQxJ6z+4nTGHfAuvJM5jeBKbfYLo\nq0FPbiLiAX4NnEHkgSIXi8jBXdS7HRiYyU4m4cxacTxeXxbVW190O5QhpWrzU2QXHMn098a5HYox\nPeZGy20msFpVN6pqEHgc6GzSz9XAk8C2wQzOxLdTyq+gZvt8mhtXux3KkNBUv5ym+uWcuO58t0MZ\nctS6JfvEjeQ2HNgc83pLtGwXESkDzlfVe7H2udnLWXW3Ur7mARprl7gdSlJrrFtKxfpHmFPzPbdD\nGZLE3vr6JF5XKLkbiL0XZ//LZhev+Dm38U6qtvyT2u2vuR1OUmqoWUTlxic4t+F2PHLgj/Ixxi1u\njJYsB0bFvB4RLYt1JPC4RIbFFQJzRCSoqnP3PtiC+lt2Hyh1NiPSZvd3vCYOiXg4u/ZW/uP7GaGO\nBoYNP8vtkJJGffXbbC9/jnPqf2LLaw2yLW3z2NI+j8qNy/Gn5LodTkIT1cHt1xURL/AhcAqwFXgb\nuFhVV3RR/0/AM6r6VCfb9GsjrF96qHux7D683gClYy52O5SEV7f9DaqrXuasmpstsbnolTFPEsgc\ny5vPH4mq9st/hIjoxW1v9ekYf0k7ut/iGWiD3i2pqmHgq8ALwAfA46q6QkS+JCJf7GyXQQ3QJJxT\nK76EeFLYvPp3boeS0Gq2zadm23xLbCYpuDKJW1WfBybtVXZfF3U/PyhBmYQ2e+OFvH7Q86z74A7G\nTv42kZkkpqeqt75IQ91i5lTfaInNJAV7BzBJ47g1ZzKs7Ew+fO+btDZtcDuchKCqbF79O1qb1zNn\nx/WW2OKGdVj1lSU3k1SOWnYE5zT/nK0bHqNi/cMM9j3lRNLWUs6H732DrLzpnLr1SrfDMfuwDxp9\nYWtLmqTjk1TOqv0+bxS9zKpF32bs5OtJSRvmdlhxQ1Wp3PQEzQ0rObvpTvwrBmcxauO+lVV5bocw\naKzlZpLWsR9+hDMbf8DGlXezvfxZt8OJCx3t1ax6/3pSUgs5p+6H+D2W2OKT9Tj0lSU3k9RSPdmc\n23gHjhPkw0XX01i72O2QXOGEO9iy9gE2rvgFZzZ8n+NWn+F2SKY7dv+zT6xb0gwJJ234JGE9j3m1\nj1Cx4VGGlZ1NXtGJST+AwnGCVKx/mJaGVZSO/QynVnzJPtImALtX3HeW3MyQ4ZUUTin/PKoOrwaf\nZtWi68gddhxFI85LuqkDeya1Szi14ouw1O2oTO8k9wevgWbJzQw5Ih5OXP8x4GO8OXIeqxffiD+1\nkNLRF5EWGN7t/vEs2FFL5ca/0tK0nrIxltQSl7Xc+sqSmxnSjlk5G5hNS902XpWHaW/dSm7R8RSW\nnonHkxh/Ho4TorryRWq3zcefksuxVReR4xsHy9yOzPRFsneZD7TE+Os1ZoAFvEWcsf1aVJU3h89j\n7ZKb8fqzKB55PoGsSXH5RtNUv5yqzf/ACbdSUHIa5zbcHuletb9qY+zPwJhYIsKxH54MnEybU8sb\ngX9SseExUCU9cwz5RSeRnjl+0JOdqtLStIbaba/S2rQeRAhkHsRp1deS4smEBuwWjTExLLkZ04U0\nTx4f2fK5Xa8bajbwjrxE+fqHAPD5skjPHEcgazyBzPH4/Fn9ct5wqIXW5vW0NK2ntXENHR01AAQy\nxy/KTtkAABVvSURBVHPUptPI9o6JJNd6bORj0rJ7bn1lyc2YHsr2jeGU8it2vQ46zdRvXcsHEz+k\neuuLhENNkQ27WnWCIHh9GXj9mfh8mThOCCfcQjjUSjjcQuQhGTFvZKp4fQHSM8ZwyJoJZHtPIN1b\nENlWj/3FDinWFO8L+1Mx5gD5PRkUphzGSRsO67KOqkNQm+nQRoJOEx7x45cAPgng+//27j5IjrrO\n4/j72z37GEISINmQLAlPBgiEJ8+YOzhFRQh4gkfVWeIV+HzeoZ5e4QNYpYced6B4KhQHHmqVYGEh\nSl2RUoGgGB6UaETDU4KEhwQSJEhCIiHZh+n+3h/dyc5usruTzENP935eVVMz0/ObzrdnJ7/P9MOv\n27oIbJT/gpuA9sbULTIRKNxEGsgsoN0m085kCLOuRnJDg7hrpi32IiItSZsla6FwExFpMa4DSmqm\ncBMRaUGtOLYyTxRuIiKtRvvcaqZwExFpOY72udVG4SYi0pIUbrXIfbi5x1mXICJSZ9osWav8hxsK\nNxEpFnfP1ZW4zezfzOwxM3vEzG42s3Yzm2ZmS83sj2Z2l5lNqWh/qZmtMbPVZnZGxfST03k8aWbf\nrKWm3IdbTJR1CSIideZYTjZLmtks4BPAye5+PMnJQc4HLgF+7u5HAfcAl6bt5wPvBo4BzgKus6FD\nQ68HPuTu84B5ZnbmvtaV+3BLzs0nIlIkuTugJAQmmVkJ6AI2AOcCN6av3wi8K318DnCLu5fdfS2w\nBlhoZjOBye6+Im13U8V79lr+w01rbiJSMMlmyXx0z+7+AvDfwHMkobbV3X8O9Lj7xrTNi8CM9C2z\ngecrZrEhnTYbWF8xfX06bZ/k49Mbg8JNRIonztNmyakka2lzgVkka3D/yO5HxTT1KJncnzg51mZJ\nESmaBh1QsvrpKeM3qhCt/BXxyl+P1+x04Bl33wxgZv8H/A2w0cx63H1jusnxpbT9BuCQivf3ptNG\nm75Pch9uWnMTkaLxFtnnFp54CuGJp+x6Ht30tT01ew5YZGadQD/wNmAFsA14P/AV4H3A7Wn7JcDN\nZvYNks2ORwK/dXc3s61mtjB9/4XANftau8JNRKTl5OdoSXf/rZn9GPgDMJje3wBMBm41sw8C60iO\nkMTdV5nZrcCqtP1F7rvON/Yx4HtAJ/Azd79zX+vKfbjFXs66BBGR+srRASUA7v4l4EsjJm8m2WS5\np/ZXAFfsYfpDwIJ61JSfT28UWnMTkaJJTk6RjzW3VpX7cIvRmpuIFIy7LnlTo9yHmwZxi0jxtMYB\nJXmW+3DTmpuIFI2Tr31urSj3n54OKBGRwvH8DOJuVbkPNx1QIiLFozW3WuX+09Mlb0SkaFxrbjXL\nf7jpgBIRKZqcXc+tFeU/3LRZUkQKxylA95yp3H96ulipiBSNa5xbzXIfbtosKSLFozW3WuX+01u3\nYCDrEkRE6so91j63GuU+3LTmJiLFE2MWZl1ErhUg3DSIW0SKJRkKkPvuOVO5//S05iYiReMeaRB3\njfL/6SncRKRoXJsla5X7cItjbZYUkWJxNBSgVrkPN625iYjISLkPNx1QIiLF41kXkHu5DzdtlhSR\n4tEmyVrlPtzcB7MuQUREWkz+wy1WuImIyHClrAuolfa5iYhUZ8aj3TW9/7k61dEMuV9zi7XmJiIi\nI+Q+3DzWUAARERku/+GmA0pEpHA0FKBW+Q83DQUQkcLRUIBaZRJuZrbYzJ4wsyfN7HN7eP29ZvZw\nenvAzBaMNi/XLxwRERmh6eFmZgFwLXAmcCxwvpkdPaLZM8Cb3P0E4HLg282tUkRE8iyLNbeFwBp3\nX+fJDrNbgHMrG7j7cnffmj5dDsxuco0iIpJjWYTbbOD5iufrGTu8Pgzc0dCKRESkUFp6ELeZvQX4\nAHBq1rWIiEh+ZBFuG4A5Fc9702nDmNnxwA3AYnd/ZbSZbd64jOV9lyUz6jiN3s7T6lmriEjTrO9b\nxvr+ZWzuX8ZTWReTc1mE2wrgSDObC/wJeA9wfmUDM5sD3AZc4O5PjzWzA3pOY9GWyxpUqohI8/R2\nJj/QX54ac+SCL/LMY/+RdUm51fRwc/fIzD4OLCXZ5/ddd19tZh9NXvYbgC8ABwDXWXI52kF3X9js\nWkVEJJ8y2efm7ncCR42Y9r8Vjz8CfKTZdYmISDHk/gwlIiIiIyncRESkcBRuIiJSOAUIN51bUkSK\nRv1arQoQbjp7togUjfq1WhUg3ERERIZTuImISE3MLDCz35vZkvT5NDNbamZ/NLO7zGxKRdtLzWyN\nma02szMqpp9sZo+kl0L7Zq01KdxERKRWnwRWVTy/BPi5ux8F3ANcCmBm84F3A8cAZzF0og6A64EP\nufs8YJ6ZnVlLQQUIN+14FZGiyU+/Zma9wNnAdyomnwvcmD6+EXhX+vgc4BZ3L7v7WmANsNDMZgKT\n3X1F2u6mivfskwKEm4iIZOgbwGcYnsg97r4RwN1fBGak00de8mxDOm02yeXPdhrvUmjjUriJiMg+\nMbN3ABvdfSVjH+LZ9FXRlr6eW3V0yKyIFEfsEWat0TX3rbmfvqfuH6vJKcA5ZnY20AVMNrPvAy+a\nWY+7b0w3Ob6Utt8AHFLx/p2XPBtt+j5rjU9QREQAiLyfIGhvyLyPXt65l+94O0x7+65nS7li2Kvu\n/nng8wBm9mbgYne/wMy+Crwf+ArwPuD29C1LgJvN7Bskmx2PBH7r7m5mW81sIcll0S4ErtnLYocp\nRLi5O0MH3IiI5FfMQMPCrYmuBG41sw8C60iOkMTdV5nZrSRHVg4CF7n7zk2WHwO+B3QCP0uvHrPP\nch9uQdBOxAAlOrIuRUSkZpH3E4T568/c/V7g3vTxZuD0UdpdASNWAZPpDwEL6lVP7g8oCcIOIu/L\nugwRkbpINkvmL9xaTf7DLegg8v6syxARqYuIASz/myUzl/9wCzu15iYihZHXzZKtJvfhFoadlBVu\nIlIQZd9BGHZlXUbu5T7cgrBLa24iUhhl306gcKtZAcKtk7LvyLoMEZG6ePbYbYTh3o5Hk5FyH25h\n2EUZrbmJSDHE0Q6tudVB7sMtCDtYO39b1mWIiNRFVN5BWFK41aoA4dZFFGmzpIgUg9bc6iP34RaG\nXcQKNxEpiCjaThh2Z11G7uU/3EqTiMqvZV2GiEhdROXthKVJWZeRewo3EZEWEmmzZF3kP9zCSUTR\n9qzLEBGpE13lpB5yH25B2E4cD2RdhoiItJDch1tCv3JEpCjUn9VDQcJNRERkSEHCzcdvIiKSC+rP\n6qEg4abVeBERGVKQcBMRERlSkHDTaryIiAwpSLiJiIgMKUi4aZ+biORf2fsIAl3LrR4KEm7grk2T\nIpJvA/FWSu37Z11GIZSyLqAewrCbsm+nzXSyURHJr774FdrapjVs/kffX1uXv7ROdTRDIdbc2joO\noC/elHUZIiI1+eNxL9LeOT3rMgqhEOHW3nEQO+KXsy5DRKQmA/0v0d5xUNZlFEIhwq2t4wCenL8x\n6zJERGoy0Pdn2jq05lYPhQi39o6DGOzXZkkRybfB/k1ac6uTQoRbW8dBDA5os6SI5FvsgwRhe9Zl\nFEIxwq1tGoP9r2RdhoiItIhChJsFIe5R1mWIiNRG43XrphDhBoAuyy4iOTYQb0Pnya2fQgziBoij\nPnZEm+gKD8y6FBGRUfXHW3no6N/x2l9W07/jxV3Tw9J+9Mz6hwwrK5bChNth8y/hrkcvY/YRH+IN\nj56YdTkiIsP0x1u4Z/p1RFEf0/xvmT77HDq6Dsa01akhChNube1TOOqkr/Hs6q9y32HP8qZn/z7r\nkkREGIhf5Z4Z11Muv0rvoR+hs3tW1iVNCIUJN0gOLDn82Ev509pbuHP6VZz50qf1q0hEMjEYb+ee\nnusZGNhE75wP0zVpTtYlTSiFCredDj70PWzdtIIlOy7m7L9cTlvQnXVJIjKB3HfYbWz584P09v4T\n3fsdnnU5E1JxjpYcYcqBb2DuUZ/iJ5M+Tdn7si5HRCaIpT1X4x4z76QrFWwZKmy4AXR2z+Kw+Z/l\njimXZV2KiEwAS3uuoWu/w+k5RPv8s1bocAPo7O5l6kF/zb2H/ijrUkSkwO6eeS1dkw5l+qyzsi5F\nmADhBjB99jt49ZWHWfn6tVmXIiIFdPfM/6Gju5fps8+u2zxdZyupyYQIN4DD5n+OZ1ddRazTdIlI\nHd198HV0dB3MjNl/V9f59r22rq7zm2hyH27l8mtVtQtLXfQe8UHu7rm6wRWJyETxi1nfpqOzhxm9\n59R93qsO/HXd5zmRZBJuZrbYzJ4wsyfN7HOjtLnGzNaY2UozG/WUI4/3PlT1vzt52gmYlVh+zK/2\noWoRkUTkAyyb8wOCsIMZvec25N8YfPGJhsy3Earp05ut6ePczCwArgXeBrwArDCz2939iYo2ZwFH\nuPvrzOyNwLeARXua32sP/Yh45qKqr4F0yOv+mefXfIufTL0zOQN3xSDvtrYptHf20N45g3mPz6Qj\nmEJHMJV225+k7Naxvm8ZvZ2nZV1G003U5YaJu+x7u9zuTswgZe8jSm87H5fpo+w7WDt/G1G0nTja\nQRTtSO7LfUTR9mr+BYKgnf3C45jR+859Xq6xPDLn99iW1upzRlNNn56FLAZxLwTWuPs6ADO7BTgX\nqPwgzgVuAnD335jZFDPrcfeNI2e2/2kf5zf3X8KiGVdhQTjuP24WMGfeRbtNd3fKg1sY6HuJ/r6X\nWDXvScqDWykPbKE8+CpODBh7Pmt3EpBh2EVY6iIIuwnDbsJSN0HYxeGPT6ZkXYTWSWidlKyTkPTe\nOgls/LpHWt8/QTu6CbrckM2yJ0FRJvYBIgaIfZDIB4jTxzGDxF5O25Tx9H7dgn7ieBD3Ml5xH8eD\n6X0/cTyQPI76cZzk/9buZxTavHEZB0wdbbn3/J7ASgRhF0HYQRh2EYSdQ7egk1JpfzrCmQRhV/J6\nqWvX46x/yD56+GO89uDNTH3nl9l6x39mWkuVqunTmy6LcJsNPF/xfD3JhzNWmw3ptN3CrXTgXPZb\ndCHLH7yY7pPO47gNCwnDzr0uysxoa59GW/s0Ju1/1F6/392HfgWWtxNF24nS+2eP3UYUv0wc9Q3d\n4j6icnLvHu9pjuzpP+3O1zb338fLU0e+b/h7gqANC9oJrIQFbVhQwiy5BcOeh5iFYCGBlcBC5j7W\nTkApuVkJS+8D0vcTYoTpa0P3yS1I5kkwdMvBadDcHSdObxHuETFR+jidRpQelBQTUx7RJkqmEaUd\nfbSro/e4nHTwXibe+Tgu4z5Y8VpUcYvB4/RxGfeYzf33jvibj/YdGfu7s7fvCayEhR3pfTtB0I5Z\n29D3q+J7tfM7FVgHYWlS0mbn9y+9D4JkHkHYkX4/28b8YfoUcOSCL476epE8Nu9Jti37DlPPvbyq\nH+stopo+vekKcfqttplHs/8Zn2Fg7Qp+t+2reLkfgNL0wzlmyyl0T57X8F9jZkZYStbW6Gj8ZXee\n4stj/od3j/G4TOyDeDxAHA+CRxXTyhW/qqPdbuuOG8B9+1DnOrJDrmwfl4d1wkmnHAPxUEe9Tx3u\n7nbv4PdmXuN37GZJMLMzoC1MpgXDp2HBsB8GFoTDn1d29EEHVpqUtmtLp+0eCJU/Mnb9u+m8SL+/\nE6WTnyjcnf4dL7Bq+nIGX3ic+P6/MPWcL2FhIbrmTFmzx1KY2SLgMndfnD6/BHB3/0pFm28Bv3T3\nH6bPnwDePHKzpJlpIIiIFJq712Wzh5mtBebWOJuN7j5zxHzH7dOzkMXPgxXAkWY2F/gT8B7g/BFt\nlgAfA36YfnBb9rS/rV5/dBGRonP3Qxs062r69KZreri5e2RmHweWkgxF+K67rzazjyYv+w3u/jMz\nO9vMngJeAz7Q7DpFRGR8o/XpGZfV/M2SIiIijZaLgRT1HPSdJ+Mtt5m918weTm8PmNmCLOpshGoH\nhZrZG8xs0MzOa2Z9jVLld/00M/uDmT1mZr9sdo2NUsX3fX8zW5L+H3/UzN6fQZl1Z2bfNbONZvbI\nGG0K1781nLu39I0kgJ8i2RHaBqwEjh7R5izgp+njNwLLs667Scu9CJiSPl5chOWudtkr2v0C+Alw\nXtZ1N+lvPgV4HJidPj8o67qbuOyXAlfsXG5gE1DKuvY6LPupwInAI6O8Xrj+rRm3PKy57Rog6O6D\nwM4BgpWGDfoGpphZT3PLrLtxl9vdl7v71vTpcpLxJkVQzd8c4BPAj4GXmllcA1Wz3O8FbnP3DQDu\n/nKTa2yUapbdgcnp48nAJncvN7HGhnD3B4BXxmhSxP6t4fIQbnsaIDiyEx9t0HeeVbPclT4M3NHQ\nippn3GU3s1nAu9z9eqodJNf6qvmbzwMOMLNfmtkKM7ugadU1VjXLfi0w38xeAB4GPtmk2rJWxP6t\n4TRSsADM7C0kR5SemnUtTfRNoHK/TFECbjwl4GTgrcAk4EEze9Ddn8q2rKY4E/iDu7/VzI4A7jaz\n4919W9aFSevJQ7htAOZUPO9Np41sc8g4bfKmmuXGzI4HbgAWu/tYmzbypJpl/yvgFkvO63UQcJaZ\nDbr7kibV2AjVLPd64GV37wP6zOw+4ASS/VV5Vs2yfwC4AsDdnzazZ4Gjgd81pcLsFLF/a7g8bJbc\nNUDQzNpJBgiO7MCWABfCrtHyexz0nTPjLreZzQFuAy5w96czqLFRxl12dz88vR1Gst/topwHG1T3\nXb8dONXMQjPrJjnAIPMxRXVQzbKvA04HSPc5zQOeaWqVjWOMvvWhiP1bw7X8mptP0EHf1Sw38AXg\nAOC6dA1m0N0zP2Fprapc9mFvaXqRDVDld/0JM7sLeASIgBvcfVWGZddFlX/zy4HvVRwy/1l335xR\nyXVjZj8ATgMONLPngH8H2ilw/9YMGsQtIiKFk4fNkiIiIntF4SYiIoWjcBMRkcJRuImISOEo3ERE\npHAUbiIiUjgKNxERKRyFm4iIFI7CTURECkfhJjIKM+s2s9Vm9hszCyumn2FmkZn9S5b1icjodPot\nkTGY2YkkF4L9urt/Pj1h70rgQXc/L9vqRGQ0CjeRcZjZp4CrgMXAZ4BjgROKcNJekaJSuIlUwcx+\nSnKB0DbgdHdflm1FIjIW7XMTqc73gQ7gYQWbSOtTuImMw8xmAlcDDwEnmNm/ZlySiIxD4SYyvhuB\nHSRXgb4auNLMjsu2JBEZi/a5iYzBzC4GrgTe4u4PmFkbydGTHcDr3b0/0wJFZI+05iYyCjM7Cbgc\n+C93fwDA3QeB84G5wNczLE9ExqA1NxERKRytuYmISOEo3EREpHAUbiIiUjgKNxERKRyFm4iIFI7C\nTURECkfhJiIihaNwExGRwlG4iYhI4fw/t3kM0QS9/tUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22b3def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = 7\n",
    "pyplot.figure(figsize=(size, size-1))\n",
    "pyplot.title('Total Velocity field')\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "pyplot.xlim(0, 1)\n",
    "pyplot.ylim(0, 1)\n",
    "\n",
    "Vtotal= numpy.sqrt(u**2+v**2)\n",
    "#Vtotal= numpy.abs(v)\n",
    "pyplot.contour(X, Y, Vtotal, 15, linewidths=0.5, colors='k')\n",
    "pyplot.contourf(X, Y, Vtotal, 15, cmap='rainbow')\n",
    "                  #vmax=50, vmin=0)\n",
    "pyplot.colorbar()  # draw colorbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3991.53526449,  4697.23214482,  4427.94528366,  4213.10279637,\n",
       "        4056.60881842,  3935.0524005 ,  3835.61636196,  3758.7042195 ,\n",
       "        3678.83215419,  3624.28803091,  3574.58365565,  3512.86339586,\n",
       "        3477.97481989,  3446.47687207,  3393.90113729,  3365.99608357,\n",
       "        3346.67520352,  3314.63917316,  3278.95525446,  3263.89905598,\n",
       "        3249.45519765,  3219.13221798,  3199.8855574 ,  3188.04140691,\n",
       "        3175.46727089,  3157.2693224 ,  3144.22991707,  3132.72142655,\n",
       "        3123.92635749,  3118.96690841,  3108.32799206,  3098.56287193,\n",
       "        3101.19357179,  3101.62102519,  3094.55662065,  3093.65095622,\n",
       "        3110.17941018,  3111.68331515,  3115.93189462,  3141.31882334,\n",
       "        3156.43775968,  3175.3078    ,  3215.96547724,  3250.99279038,\n",
       "        3305.71980505,  3375.05945207,  3470.19304338,  3601.64257033,\n",
       "        3743.87730196,  3061.23226415])"
      ]
     },
     "execution_count": 811,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAEeCAYAAADb1FGVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcHVWd9/HPNyEkBMIaIRAgghAIjCxBMwyoNAaJLAOo\nI09cAAWdEZgRfVwgqENwQeAZFBwHdATZFMMm2xBDQNIyCISAIEuAhC1kgUhICEI0Zvk9f5xzk0qn\nu9Mdum/V7f6+X69+dd1za/nVvVX1q3PqVF1FBGZmZlXVp+wAzMzM2uNEZWZmleZEZWZmleZEZWZm\nleZEZWZmleZEZWZmleZEVTGShklaKeltfTeS/izpnV0TVfeRdIKk/y07jtZIGi7pEUmLJf2rpMsl\nfbsbl3eQpNndNf/OkPSCpA+WHYe9PZLGSPp14fUBkmZIekPSUes5zymSTuyC2G6QNKYj4zZ0opL0\noqQl+UCyUNK9kv5FksqO7W162ze3RcSgiHgRoLsPsB3VThIu/Wa+Nj6jrwN3R8RmEfHjOoVS+mdh\n1deJk5rvAt8vvP428KOI2DQibm0r6UjaX9LvuyreNpwHfK8jIzZ0oiLt1EdExGbAMOBc4HTgsvWZ\nmaS+XRibrU2k76xRTiSGAU+WHUQj8T5UN7V9qe0RpPcAm0bEtELxMGB6B+Z/BHD7+oe3bjmuQZJG\ndmTkhv0DXgA+2KLsvcAKYI/8+nDgD8BiYBZwVmHcYcBK4MT8XnMufx/we2BRLj8eeA/wCqDC9B8F\nHm0lrlHAyy3G/Qjwxzws4AzgWeBVYAKweSGmFUCf/Hpb4BbgNWAG8LnCPPsAZ+b5LAamAUPzeyuB\nnYHPA38D/gq8kef1VeCGFjH/CPhhG5/z7sCU/Hk8Dvxj4b3LgR8D/5Pnfz+wUxvzmZXX7c953L8H\nTgD+F/h/wELgOeDDhWk2BS4F5gGzge8UP9cW898QuBCYC8wBfgj0y++dAPxvi/Hb+4x+CywH/pLL\ndsnr+u3C9J8HZgILgJuBIbl8POmsFWAD4E3gvPx6QJ7n5q3Ef1Bex3F5u3ge+GSLz+Iq4E+kbf8b\nhffOAq5uZduubUdTSGfT9+b1mQRsWRj/OODFvNwzKexbpH3qvvz9zwX+E9igxed4Cmn7fC5vD//R\nYt1uAU7rwD79deCBQtwnk7a5DdsY/2jgEdL2PxM4tAP7zVnAdcDV+bP4I7AraZ+cT9pOP1QYfwpw\nDjA1L+em4vcHHAU8Qdp+7wZ2b3GM+kpexiLgV8V1AY7M8S/K382725l2AmkbHwgsIW2ftX1pSCuf\nzbeA/y68fjZPsyRPc06L1z8qjPswsE8e/hDwVI7hP4Fm4MT83s6kfWUBabv8BSk5QgeOM8B/A99a\n53axrhGq/EcriSqXzwL+JQ9/ANgzD/8dKYEc1WJnvgLYCOgP7Ji/tGOBvsAWwF55/CeAMYXl/Br4\nUhuxzQRGF15fB3wtD59G2vG3BfoBlwDXFGIqJqp78sbRD9g7bwxN+b2v5Y14l/z63cAWeXgFsHMe\nbnmAHZI38NoG1Ze0g+7TynpskNfl9Dx8cP58di3M+1VgP1Li/EVtXVqZV23dign8BFKSOJGUwL8A\nzC28fxNwMekAP5h0EPt8G/P/dv5ct8p/vwfOLiznnhbjt/kZ5bIp5B2y5TjAB/N6752/mx8Bv8vv\nHczqk5J/IB0g7i9M90gb8R8ELCMl7X6kbffNwmd9Vf48BubP8hngs/m9s4CrWvmsi4lqJvAu0nY+\nBTgnv7dH3h4OzMu9IH8ntUQ1knTyJdL+8STwxcKyVgJ3AJvleb8XmFN4f6u8HoM7sE+LdCD8d9LJ\nwULy/tfKuKOA1wtxbgsM78B+cxbp4HwIaZu9knRSMI60L3wOeL7FdjAbGEE6TtxAPikAhud1+2Ce\n9mv5c96gcIx6ANgG2JxUm/nn/N6+pP3uPXm9j8vj9+vAtAcBL63js7wO+Eorx8yD29rGC8eH2Xl4\nMGl//0hevy+RttFaonoXMJp0bNgqf3c/6OhxBvgyLZJZq+uyrhGq/Efbiep+YFwb0/wQuKDFzjys\n8P4ZwI1tTPt14Bd5eEvgLWCbNsb9DnBZHh6UN+bt8+vpLTaWbUkHhj6FmPoAO+SNYmBh3HOAn+fh\np4Ej21j+Sto/CN8OnJSHjwSeaGM+7wPmtSi7Bvj3wryLZ22HAdPbmNcaB89cdgIwo/B6oxz71vnv\nr0D/wvtjSdeNWpv/s6x5InEo+YBD64lqXZ9Re4nqUuDcwnsb5+9wR1JSXUI6yTmddAB8iZRgxgMX\nthH/QXkeAwpl1wLfyNvDUmC3wnv/XPss6FiiOrPw/snAxDz8LQonFznOpbSyb+X3T6Owj+TP8aAW\n4zxJPlEDTgX+pxP79TBSTWg68PV2xvsJeV9uUb497e83ZwF3FN47knQwVn69SV6n2gF2VVLPr0fk\n7VLAN4EJhfdEqs1/IL9+AfhE4f3zgIvz8MXkE6nC+08D7+/AtB1JVJPJia1QtsYxk9YT1YnAz/Lw\nccB9Ld6f3XKawntHAw8XXrd7nCGdFNy1rm2i0a9RtWUo6UwMSX8v6W5Jf5L0OvAvpLOEojmF4R1I\nzRet+QVwpKSNSDWueyJifhvjXgN8RFI/UhPhwxFRW84w4KbcAWQhaYdcRjpzKtoWWBgRSwpls/L6\n1WJ9vo3lr8tVwKfz8KdIzSCt2Y60YRYVY4DUJFqzhLSjd8aq6SPiL3lwE9Ln1A94OX9Wi0gHp5bf\nXzHWl1rEuV0nY+mo7fL8AYiIt0gH16ER8VfgIaCJVCtqJtX03kc6wPyunfkuytPX1NZhMOmzaLl+\nxe9hXdr6ntb4jvP29lrttaRdJd0m6eW8D32P9vchWHP7+jRtb19riYhZpAPoMNLBvC1t7avb0f5+\nA+nMvuYvwILIR878Gtbcjov7wCzSdzGYtbeDyOO2tazi5z4M+ErtOJC37+1Zc5tta9qOWEQ6Se6s\nw4GJebi1/X/Va0lbS/qVpDl52/gFa24b6zrODCLVitvV4xKVpPeSPtxal+dfkq4fDI2IzYGfsvbF\n/CgMzyY1OawlIuaRamsfYx07X0Q8RdqADwc+QUpcNS8Bh0XElvlvi4jYOCJebjGbecCWkjYulO1I\nuk5Qi/VdbcVQDKeVspuBvSTtSTrT+WUb084jHRCKijF0RmtxtGc26cx1q8LntHlE7NXG+HNJO3/N\nMFL8kGq/A2tvSBryNmObV1xW/o62YvXncg+pOWgf0rXDe4AxpGaxe9qZ7xb5RKhmx7ysBaSTmZbr\nV1veGutHOsnpqJcpfMeSBuZ1qbmEdI3iXXkf+gbt70OQDlhHS9qLdI3z5o4GI+kIUpPpb4H/aGfU\ntrb/de0366O4DwwjfRcLaLEdFMZtmbhbMxv4XovjwCYRcW0Hpu3I9voYqWmyw/ORtAHpZOrOXPQy\n6bMrKn4W55Bqn3vmbePTrLltrOs4M4J0+aJdPSZRSRok6UjSxcqrI6LWs2UT0lnqMkmjgE+2nLTF\n618CoyX9k6S+kraUtHfh/atJTYB/R7pG1Z5rSM0k7weuL5T/FDhH0o459ne0uKdBALkGdh/wfUn9\n805/EqsT5KXAdyTtkufzbklbtBLHfNJFz1UiYilwY45xaqG219JUYImkr0vaQFITaYP71TrWvTWv\nkjbqjiRXIuIVUvPFD/P3K0k7S/pAG5NMAL4pabCkwaQmrdpn9UdgT0l7SepPav4p7qRrfUbr8Cvg\ns4X5nQM8EBG1Gs/vSJ1wpkfEclKt6nPACxHxWmszzAScLamfpPeTel9dFxErSc2A35O0iaRhpPb9\n2vo9CnxA0g6SNiM1YXfUDaSWggNyC8C3WXO/GAS8ERFLJO1OajZsV0TMJdUqryY1Ey5dtYLpVoCf\nt7ry6Xv7Gan56TM5rsPaWMxlpO/g4LxtbCdptw7sN+vj05J2z0n8bOD6XHu6Djgix7CBpK+STq7u\n78A8fwZ8IR+XkLSxpMNbJNi2zAe2krRpO+NMJNXq1zWf4nb/PtL11Tfz69uBPSQdk4+Hp5GuPdXU\nLmv8WdJQ0jW6VTpwnDkI+M06YuwRieo2SYtJtZRxpDOw4n0Bp5AO5otJ7cktz1bWOKOIiNmkWtBX\nSc2HjwDFM/ibSGdQv27RRNOaCaSmn99GxMJC+UWkHkmTc1z3kS4MtxbTJ4CdSGduN5J6yEzJ7/2A\ntKPU5nMp6RpPy3lcRjpIL1Th5j/SReR3k6rnrYqIZcA/kj6TBaQeXcdFxMxWltOu3Kz3PeD3OZZR\nbY1aGD6e1NNpOun7uJ41d5Si75IOjo+REtNDeXnkeL9NOkufweoad01rn1HLdVv1OiJ+S0qEvyad\nqe9Eun5Wcx/pWtXv8vjTSU1K7TX7QTqDXUT6vq8mdQqqfdZfJDX/PE+qlf0iIi7P87+LtG0/RqrB\n3dZW7C3l2E4lJd95pGa/4gHlq8CnJL1BOsma0MF5X0k6oWu5fe1A6uHWmp8CN0XEHXmf+Rzws9ZO\nwCJ1b/4sqafnYtLJQO3s/5O0vd90RMt1ujqvzzzS9nhajmEGqRbxY9KJ2BGkXrHL25hPMf6HST1H\nf5wvAcwgXUttK4bitM+Qvq/n8za71j4REY8Ar+dWprbmeRHwcUmvSbowxz+xMI/XgI+Tro8tIJ1k\nFr+7s0kdqV4nbXM3thJuq8eZHNefI+KhttZz1birm2WrQ9KLpA1vJbAsIkblDfVaUpJ4ETg2Ihbn\n8ceRktNyUhfYybl8JKlH3wDSheMvdVF8z5IuUt7dFfMri6QdSE06QwpnUGZdItcIr46IdxbK+pFq\nf3tFxIqyYusMSVNI69FqLbDKJH0IODkiPtrB8Z8EPhYRT3dhDK0eZyTdAFwaEZPWNY+q1qhWkrqS\n7hsRtbPuM0i9Q3Yj3aswDkDSHqSODSNIPc4ullY9meISUo+T4cBwdfBxHe2R9DFgZQ9IUn1I92hM\ncJKyrpYT0mmk5q1VImJZROzZKEmq0UXEnZ1IUv2AK7s4SbV5nImIf+pIkoLU972KxNpJ9GhSeyak\nqmQzKXkdRfoQlgMvSpoJjJI0CxgUq+/Kvgo4hnS/x/oFlc6sRrC6F0tDyu3s80ldVdtq/zdbL/k6\n1kOkZvOLSg6nK1Sv2akb5Gb+87tqfl15nKlqogrgTkkrgJ9GxKWk+5XmQ7rILmnrPO5Q1rxwOTeX\nLWfNdvY5dK4r79pBRRz8dqavitxtd326rZqtUz4j7+wtCpUVEX4473royuNMVRPVgRHxsqR3kDoK\nPEM7F7bNzKznqmSiqt1PFBGvSrqZ1CNuvqRtImJ+7uHypzz6XNbs1799LmurfC2SnPTMzNZDRHT7\nQ6Yr15lC0kBJm+ThjUmPwXkcuJV0XwWkLpy35OFbgbGSNpS0E+lm3QfzPTiLJY3KnSuOL0yzlpaP\n7Gikv7POOqv0GBx/+XH0ttgdf/l/9VLFGtU2pMcLBSm+X0bEZEkPAdcp/XbKLFJPPyJiuqTrWP0Y\nolNi9Sd4Kmt2T+9QDxMzM6uOyiWqiHiB9NiZluULSU87bm2a77Pmj4PVyh8m3WhmZmYNqnJNf9Z5\nTU1NZYfwtjj+8jRy7OD4e4tKPpmi3iSFPwczs86RRPTGzhRmZj3Ro688ynt/9l7umdXew/OtNU5U\nZmZ1cPbvzuaheQ/xrSnfKjuUhuNEZWZWB3ttnX6E4eB39ogH3NSVE5WZWR0sXZF+kqt/3/4lR9J4\nnKjMzOrgr8vTz9cN2GBAyZE0HicqM7M6WLo816g2cI2qsyp3w6+ZWU90wA4HsHTFUvZ4xx5lh9Jw\nnKjMzOrguL2PY2WsZNrcaRy4w4H069uv7JAahm/4xTf8mll9DPmPIcx/az5zvjyHoZu+rZ/HqwTf\n8Gtm1sMM2WQIAK+8+UrJkTQWJyozszpxolo/TlRmZnVSS1Tz35pfciSNxYnKzKwObph+AxNnTgRc\no+osJyozszr4zj3f4dUlr3LQsIPYf/v9yw6noVQ2UUnqI+kPkm7Nr7eQNFnSM5LukLRZYdxxkmZK\nekrSoYXykZIekzRD0oVlrIeZGax+MsVPjvwJH9zpgyVH01gqm6iA00g/L19zBnBXROwG3A2MA5C0\nB+ln6UcAhwEXS6p1l7wEOCkihgPDJY2pV/BmZkWrnkzhZ/11WiUTlaTtgcOBSwvFRwNX5uErgWPy\n8FHAhIhYHhEvAjOBUZKGAIMiYloe76rCNGZmdbXqobR+hFKnVTJRAT8EvgYU78LdJiLmA0TEK8DW\nuXwoMLsw3txcNhSYUyifk8vMzOquVqPyQ2k7r3KPUJJ0BDA/Ih6V1NTOqF36KInx48evGm5qaqKp\nqb1Fm5l1znF7Hccbf3uDgf0Glh3Kemtubqa5ubnuy63cI5QknQN8GlgObAQMAm4C3gM0RcT83Kw3\nJSJGSDoDiIg4L08/CTgLmFUbJ5ePBQ6KiJNbWaYfoWRmdfHQvIe46ambeO/Q93LM7o19NaLXPkIp\nIs6MiB0jYmdgLHB3RBwH3AZ8Jo92AnBLHr4VGCtpQ0k7AbsAD+bmwcWSRuXOFccXpjEzK8UjLz/C\nOfeew/XTry87lIZRuaa/dpwLXCfpRFJt6ViAiJgu6TpSD8FlwCmF6tGpwBXAAGBiREyqe9RmZgXv\n2e49QKpZWcdUrumvDG76M7N6WbZiGYO+P4ilK5by6tdeZfDAwWWHtN56bdOfmVlP1q9vPw7Y4QAA\nml9sLjeYBuFEZWZWZ6N3Gg3A3S/cXXIkjaGRrlGZmfUIHx3xUbYauBWH7HxI2aE0BF+jwteozKyx\nrVi5giEXDGH5yuUsOn1R3ZZbr2tUrlGZmTW4vn36sugvi1gRK1i+cjkb9OlZh3ZfozIz6wFqzxCs\nPaqpJ3GiMjNrcF+a9CWWLFsCrP45kZ7EicrMrETLVizjlqff3kNzrnn8mlXDtae09yROVGZmJVkZ\nKzng5wdwzLXH8JuZv1mveSxYsoBXl7y66rVrVGZm1mX6qA8f3+PjAPzwgR+u1zymzU0/uTdi8AgW\nnb6Id27+zq4KrzKcqMzMSvS5kZ+jf9/+3Pn8nVz7xLWdnn7q3KkAHL7r4Ww+YHP6qOcd1nveGpmZ\nNZAtN9qSc0afA8DxNx/PMwue6dT0k5+bDMD7d3x/l8dWFU5UZmYl+/L+X+aEvU/gbyv+xoQnJnRq\n2huPvZGfH/VzRu88upuiK5+fTIGfTGFm5fvTW3/ikZcfYcwuY8oOpcPq9WQKJyqcqMzM1od/5sPM\nzDrs87d+ns3O3Ywbpt9QdihdrnKJSlJ/SVMlPSLpcUln5fItJE2W9IykOyRtVphmnKSZkp6SdGih\nfKSkxyTNkHRhGetjZvZ2XP7I5ex/6f5cPO1ibp9xOz956CdMeWHKWuMtXbGUN5a+wVt/e6uEKLtX\n5Z5cGBFLJR0cEUsk9QV+L+k3wMeAuyLifEmnA+OAMyTtQfpZ+hHA9sBdknbNbXmXACdFxDRJEyWN\niYg7Slo1M7NOWbFyBefcew7PLnx2VTd0gI37bcxVH7mKj4746Kqy/n3zs/564JMpKpeoACJiSR7s\nT4oxgKOBg3L5lUAzcAZwFDAhIpYDL0qaCYySNAsYFBHT8jRXAccATlRm1hD69unLH/75D1z75LXc\n9PRNLP7rYv78tz/zsREfY98h+64x7n7b7cdrf3mNHTfbsaRou08lO1NI6gM8DLwL+K+IGCdpUURs\nURhnYURsKek/gfsj4ppcfikwEZgFfD8iDs3l7wO+HhFHtbI8d6YwM+ukXv17VBGxEthX0qbATZL2\nJNWq1hitK5c5fvz4VcNNTU00NTV15ezNzBpec3Mzzc3NdV9uJWtURZK+BSwBPgc0RcR8SUOAKREx\nQtIZQETEeXn8ScBZpBrVlIgYkcvHAgdFxMmtLMM1KjOzTuq13dMlDa716JO0EfAh4CngVuAzebQT\ngNpz8W8FxkraUNJOwC7AgxHxCrBY0ihJAo4vTGNmZg2iik1/2wJX5utUfYBrI2KipAeA6ySdSKot\nHQsQEdMlXQdMB5YBpxSqR6cCVwADgIkRMam+q2JmZm9X5Zv+6sFNf61bsAC++EXYdlu44IKyozGz\nqvEjlOrIiap1zz4Lu+4KO+8Mzz1XdjRmVjW99hqVVcdf8w+FDhhQbhxm1rs5UVmbluYb3Pv3LzcO\nM+vdnKisTU5UZlYFTlTWJjf9mVkVVLF7ulXEHnvAL34BgweXHYmZ9Wbu9Yd7/ZmZrQ/3+jMzM8OJ\nyszMKs6JyszMKs2JyszMKs2Jytp0883wqU/B9deXHYmZ9WZOVNamP/4RrrkGnnii7EjMrDdzorI2\nLVmS/g8cWG4cZta7OVFZm2qJaqONyo3DzHo3Jyprk2tUZlYFlUtUkraXdLekJyU9LumLuXwLSZMl\nPSPpjtrP1ef3xkmaKekpSYcWykdKekzSDEkXlrE+jcyJysyqoHKPUJI0BBgSEY9K2gR4GDga+Czw\nWkScL+l0YIuIOEPSHsAvgfcC2wN3AbtGREiaCvxrREyTNBG4KCLuaGWZfoRSK+67L/1g4vveBzvt\nVHY0ZlY1/oXfTNLNwI/z30ERMT8ns+aI2F3SGUBExHl5/N8A44FZwN0RsUcuH5unP7mVZThRmZl1\nkp/1B0h6J7AP8ACwTUTMB4iIV4Ct82hDgdmFyebmsqHAnEL5nFxmZmYNpLI/85Gb/W4ATouINyW1\nrPJ0aRVo/Pjxq4abmppoamrqytmbmTW85uZmmpub677cSjb9SdoA+B/gNxFxUS57CmgqNP1NiYgR\nrTT9TQLOIjX9TYmIEbncTX9mZl2otzf9/RyYXktS2a3AZ/LwCcAthfKxkjaUtBOwC/Bgbh5cLGmU\nJAHHF6YxM7MGUbkalaQDgXuAx0nNewGcCTwIXAfsQKotHRsRr+dpxgEnActITYWTc/l+wBXAAGBi\nRJzWxjJdo2rFiSfC8uXwX/8FgwaVHY2ZVY17/dWRE9XaItITKZYuhbfe8r1UZra23t70ZyV7662U\npAYOdJIys3I5UVmrXn01/R88uNw4zMycqKxVCxak/05UZlY2JyprVS1RveMd5cZhZlbZG36tXPvu\nCzfeCJtvXnYkZtbbudcf7vVnZrY+3OvPzMwMJyozM6s4JyozM6s0JyozM6s0Jypby3PPwf77w+mn\nlx2JmZm7p1srnngCpk5113QzqwbXqGwtjzyS/u+5Z7lxmJmBE5W14ve/T///4R/KjcPMDHzDL+Ab\nfouWL4cttoA334R582DbbcuOyMyqqlff8CvpMknzJT1WKNtC0mRJz0i6Q9JmhffGSZop6SlJhxbK\nR0p6TNIMSRfWez0a0WOPpSS1yy5OUmZWDZVMVMDlwJgWZWcAd0XEbsDdwDgASXsAxwIjgMOAi/NP\nzwNcApwUEcOB4ZJaztNaGDkSXnoJrr667EjMzJJKJqqIuBdY1KL4aODKPHwlcEwePgqYEBHLI+JF\nYCYwStIQYFBETMvjXVWYxtqxww6pe7qZWRVUMlG1YeuImA8QEa8AW+fyocDswnhzc9lQYE6hfE4u\nMzOzBtJIiaol934wM+sFGumG3/mStomI+blZ70+5fC6wQ2G87XNZW+WtGj9+/KrhpqYmmpqauiZq\nM7Meorm5mebm5rovt7Ld0yW9E7gtIt6dX58HLIyI8ySdDmwREWfkzhS/BP6e1LR3J7BrRISkB4Av\nAtOA24EfRcSkVpbV67unP/dc+jvkEOjTyPVsM6ub3t49/RrgPlJPvZckfRY4F/iQpGeA0fk1ETEd\nuA6YDkwETilknVOBy4AZwMzWkpQl3/0ujBkDhYqlmVklVLZGVU+9vUa1YAFstx2sWAEzZsC73lV2\nRGbWCHp1jcrq68orYdmyVKNykjKzqnGi6uXeegsuzM/s+MIXyo3FzKw1TlS93E9/CnPmwD77wJFH\nlh2NmdnaGql7unWDU0+FTTeFESPc28/MqsmdKXBnCjOz9eHOFGZmZnQiUUm6T9Jxkvp3Z0BmZmZF\nnalR/Y301PJ5kn4gafduisnMzGyVDieqiGgC9iAlq+OBJyU1S/o/kvp1U3zWDS64AC6+OP2ar5lZ\n1a1XZ4rc/Hcs8M/AAcAC0o8d/ndEPN+lEdZBb+pMMW9e+vXev/wFpk6FUaPKjsjMGlWlO1NExNKI\nuBo4Dfhf4B3A14EZkq7PTze3Cvr3f09J6mMfc5Iys8bQ6RqVpI2ATwBfAPYDniH95Pv1wD8C44Gn\nI2J0l0bajXpLjerZZ2H3fGVx+nQYPrzceMyssdWrRtXhG34lvRv4F+BTwMbALcDpETGlMNrPJL1C\nSlpWMeefnx48+9nPOkmZWePozJMp/gjMAy4kXYt6uY3xngXuf7uBWddasSLVoiQ444yyozEz67gO\nN/1J+ihwS0Ss6N6Q6q+3NP1FwMMPw3veU3YkZtYT1Kvpz49QovckKjOzrlTpXn+NRNKHJT0taUb+\nCXszM2sgPbpGJakP6WfoR5Our00DxkbE0y3Gc43KzKyTXKPqGqOAmRExKyKWAROAo0uOqa6cf82s\n0fX0RDUUmF14PSeX9QqLFsGwYfBv/+aEZWaNq6cnql7tpptg9uzV3dLNzBpRT/+F37nAjoXX2+ey\ntYwfP37VcFNTE01NTd0ZV7eLSA+eBfj0p8uNxcx6hubmZpqbm+u+3J7emaIv6RFPo4GXgQeBT0TE\nUy3G63GdKSZOhCOOgK23hhdegIEDy47IzHqayj1CqRFFxApJ/wpMJjVzXtYySfVUV1yR/n/5y05S\nZtbYenSiAoiIScBuZcdRTxGwdCn06wef/GTZ0ZiZvT09uumvo3pi0x/An/8MgwaVHYWZ9VR+hFId\n9dREZWbWnXzDr5mZGU5UZmZWcU5UZmZWaU5UPUgEfPOb8Otfw7JlZUdjZtY13JmCntOZYsYM2G03\nGDwY5s+HPj4NMbNu5M4U1mm//W36f8ghTlJm1nP4cNaD3Htv+v+BD5Qbh5lZV3Ki6kHuuy/9P/DA\ncuMwM+tKvkZFz7hGtXAhbLUVbLQRvPEGbNDjH45lZmXzQ2mtU/r1g8svh9dec5Iys57FNSp6Ro3K\nzKze3Otdca1fAAAN6ElEQVTPzMwMJyozM6s4JyozM6u0SiUqSf8k6QlJKySNbPHeOEkzJT0l6dBC\n+UhJj0maIenCQvmGkibkae6XtGM918XMzLpGpRIV8DjwEeB3xUJJI4BjgRHAYcDFkmoX8C4BToqI\n4cBwSWNy+UnAwojYFbgQOL8O8Zfi8cfhqKPgggvKjsTMrOtVKlFFxDMRMRNo2YvkaGBCRCyPiBeB\nmcAoSUOAQRExLY93FXBMYZor8/ANwOhuDb5E06fDbbetvuHXzKwnqVSiasdQYHbh9dxcNhSYUyif\nk8vWmCYiVgCvS9qy+0Otvzn5E9hhh3LjMDPrDnW/NVTSncA2xSIggG9ExG3duej23hw/fvyq4aam\nJpqamroxlK41b176v9125cZhZj1bc3Mzzc3NdV9u3RNVRHxoPSabCxTrC9vnsrbKi9PMk9QX2DQi\nFra1gGKiajRz8xoPHdr+eGZmb0fLk/izzz67LsutctNfsQZ0KzA29+TbCdgFeDAiXgEWSxqVO1cc\nD9xSmOaEPPxx4O46xV13TlRm1pNV6hFKko4B/hMYDLwOPBoRh+X3xpF68i0DTouIybl8P+AKYAAw\nMSJOy+X9gauBfYHXgLG5I0Zry23oRyg9+CA89xwcemh6MK2ZWT3U6xFKlUpUZWn0RGVmVgY/68/M\nzAwnKjMzqzgnKjMzqzQnKjMzqzQnqgb38MPw4Q/Dd75TdiRmZt3DP1re4GbNgjvugAEDyo7EzKx7\nuEbV4N56K/3fZJNy4zAz6y5OVA3ur39N/12jMrOeyomqwdUS1UYblRuHmVl3caJqcK5RmVlP50co\n0diPUHrhBXjqKRg2DPbcs+xozKw38bP+6qiRE5WZWVn8rD8zMzOcqMzMrOKcqMzMrNKcqMzMrNIq\nlagknS/pKUmPSrpR0qaF98ZJmpnfP7RQPlLSY5JmSLqwUL6hpAl5mvsl7Vjv9amHs8+GI4+EBx4o\nOxIzs+5RqUQFTAb2jIh9gJnAOABJewDHAiOAw4CLJdV6mlwCnBQRw4Hhksbk8pOAhRGxK3AhcH79\nVqN+HnwQbr8dXnut7EjMzLpHpRJVRNwVESvzyweA7fPwUcCEiFgeES+SktgoSUOAQRExLY93FXBM\nHj4auDIP3wCM7u74y+Abfs2sp6tUomrhRGBiHh4KzC68NzeXDQXmFMrn5LI1pomIFcDrkrbszoDL\n4ERlZj1d3X/mQ9KdwDbFIiCAb0TEbXmcbwDLIuJXXbno9t4cP378quGmpiaampq6cNHdx4nKzOql\nubmZ5ubmui+3ck+mkPQZ4PPAByNiaS47A4iIOC+/ngScBcwCpkTEiFw+FjgoIk6ujRMRUyX1BV6O\niK3bWGbDPplizz1h+nR44gk/QsnM6qtXPplC0oeBrwFH1ZJUdiswNvfk2wnYBXgwIl4BFksalTtX\nHA/cUpjmhDz8ceDuuqxEnf3kJ3DbbelZf2ZmPVGlalSSZgIbArU+bA9ExCn5vXGknnzLgNMiYnIu\n3w+4AhgATIyI03J5f+BqYN88v7G5I0Zry23YGpWZWVn8UNo6cqIyM+u8Xtn0Z2Zm1pITlZmZVZoT\nlZmZVZoTVYM7/HA44ghYvrzsSMzMuoc7U9C4nSkioE8+1Vi5EtTtlzTNzFZzZwpbp1otqm9fJykz\n67mcqBrY3/6W/m+4YblxmJl1JyeqBrZsWfrfr1+5cZiZdScnqgbmRGVmvUHdn55uXWfTTWHSpNUd\nKszMeiL3+qNxe/2ZmZXJvf7MzMxwojIzs4pzojIzs0pzojIzs0pzompg06fDmDHw1a+WHYmZWfep\nVKKS9G1Jf5T0iKRJkoYU3hsnaaakpyQdWigfKekxSTMkXVgo31DShDzN/ZJ2rPf6dLcFC2DyZJg6\ntexIzMy6T6USFXB+ROwdEfsCtwNnAUjaAzgWGAEcBlwsrXq63SXASRExHBguaUwuPwlYGBG7AhcC\n59dxPerCN/yaWW9QqUQVEW8WXm4MrMzDRwETImJ5RLwIzARG5RrXoIiYlse7CjgmDx8NXJmHbwBG\nd2fsZfCz/sysN6jckykkfRc4HngdODgXDwXuL4w2N5ctB+YUyufk8to0swEiYoWk1yVtGRELuzH8\nunKNysx6g7onKkl3AtsUi4AAvhERt0XEN4FvSjod+DdgfFctur03x49fvZimpiaampq6aLHdx4nK\nzOqpubmZ5ubmui+3so9QkrQDcHtE7CXpDCAi4rz83iTS9atZwJSIGJHLxwIHRcTJtXEiYqqkvsDL\nEbF1G8tqyEcozZ8Pjz4KgwfDfvuVHY2Z9Ta98hFKknYpvDwGeDoP3wqMzT35dgJ2AR6MiFeAxZJG\n5c4VxwO3FKY5IQ9/HLi721egzrbZJnVPd5Iys56sateozpU0nNSJYhbwBYCImC7pOmA6sAw4pVAF\nOhW4AhgATIyISbn8MuBqSTOB14CxdVsLMzPrMpVt+qunRm36MzMrU69s+jMzM2vJicrMzCrNicrM\nzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrNicrMzCrN\nicrMzCrNicrMzCrNicrMzCqtkolK0lckrZS0ZaFsnKSZkp6SdGihfKSkxyTNkHRhoXxDSRPyNPdL\n2rHe62FmZm9f5RKVpO2BD5F+4bdWNgI4FhgBHAZcnH96HuAS4KSIGA4MlzQml58ELIyIXYELgfPr\ntAp119zcXHYIb4vjL08jxw6Ov7eoXKICfgh8rUXZ0cCEiFgeES8CM4FRkoYAgyJiWh7vKuCYwjRX\n5uEbgNHdGnWJGn1jd/zlaeTYwfH3FpVKVJKOAmZHxOMt3hoKzC68npvLhgJzCuVzctka00TECuD1\nYlOimZk1hg3qvUBJdwLbFIuAAL4JnElq9uuWRXfTfM3MrBspIsqOAQBJfwfcBSwhJZXtSTWnUcCJ\nABFxbh53EnAW6TrWlIgYkcvHAgdFxMm1cSJiqqS+wMsRsXUby67Gh2Bm1mAiotsrAXWvUbUlIp4A\nhtReS3oBGBkRiyTdCvxS0g9ITXq7AA9GREhaLGkUMA04HvhRnsWtwAnAVODjwN3tLNu1LTOziqpM\nompFkJvrImK6pOuA6cAy4JRYXRU8FbgCGABMjIhJufwy4GpJM4HXgLF1jN3MzLpIZZr+zMzMWlOp\nXn9lknR+vpn4UUk3Stq07JjWRdKHJT2db3Y+vex4OkPS9pLulvSkpMclfbHsmNaHpD6S/pCbpxuK\npM0kXZ+3+ycl/X3ZMXWGpC9LeiLf8P9LSRuWHVN7JF0mab6kxwplW0iaLOkZSXdI2qzMGNvTRvx1\nOW46Ua02GdgzIvYh3ac1ruR42iWpD/BjYAywJ/AJSbuXG1WnLAf+b0TsCfwDcGqDxV9zGqlJuhFd\nRGouHwHsDTxVcjwdJmk74N9I17H3Il3GqHrz/uWk/bXoDOCuiNiNdB29ysed1uKvy3HTiSqLiLsi\nYmV++QCp12GVjQJmRsSsiFgGTCDd5NwQIuKViHg0D79JOkgObX+qaslPUTkcuLTsWDorn/m+PyIu\nB8g3079Rclid1RfYWNIGwEBgXsnxtCsi7gUWtSguPpjgSlY/sKByWou/XsdNJ6rWnQj8puwg1qHl\nTdDFm50biqR3AvuQemg2ktpTVBrxQu9OwAJJl+emy/+WtFHZQXVURMwDLgBeIt3G8npE3FVuVOtl\n64iYD+nkDWj1FpoG0W3HzV6VqCTdmduza3+P5///WBjnG8CyiLimxFB7DUmbkB5xdVquWTUESUcA\n83OtUDTeDeUbACOB/4qIkaT7F88oN6SOk7Q5qTYyDNgO2ETSJ8uNqks04klPtx83q9w9vctFRLtP\nvZD0GVJTzgfrEtDbMxcoPhG+doN0w8hNNjcAV0fELWXH00kHAkdJOhzYCBgk6aqIOL7kuDpqDulx\nZQ/l1zcAjdQh5xDg+YhYCCDp18ABQKOdYM6XtE1EzM/PLv1T2QF1Vj2Om72qRtUeSR8mNeMcFRFL\ny46nA6YBu0galns7jSXd5NxIfg5Mj4iLyg6ksyLizIjYMSJ2Jn32dzdQkiI3N82WNDwXjaaxOoW8\nBOwvaUD+JYXRNEZnkJa171uBz+ThE4Cqn7CtEX+9jpu+jyrLNwZvSLo5GOCBiDilxJDWKW8kF5FO\nOC6rPWKqEUg6ELgHeJzU3BHAmYUbthuGpIOAr0TEUWXH0hmS9iZ1BOkHPA98NiIWlxtVx0k6i3SS\nsAx4BPhc7lhUSZKuAZqArYD5pMfA3QxcD+xAeiTcsRHxelkxtqeN+M+kDsdNJyozM6s0N/2ZmVml\nOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZmVmlOVGZVYikgfkX\nU6dK6lsoP1TSCkknlxmfWRn8CCWzipG0D+lH6H4QEWdK2gZ4FLg/Ij5abnRm9edEZVZBkr4E/D+g\n9nTqPYG9az9rYdabOFGZVZSk20m/8dMPOCQimsuNyKwcvkZlVl1XA/2BPzpJWW/mRGVWQfnXXi8C\nHgb2lvTFkkMyK40TlVk1XQn8hfST6xcB50r6u3JDMiuHr1GZVYykrwDnAgdHxL2S+pF6AfYH9uvO\nn/w2qyLXqMwqRNK+wHeBcyLiXoD88+qfAIYBPygxPLNSuEZlZmaV5hqVmZlVmhOVmZlVmhOVmZlV\nmhOVmZlVmhOVmZlVmhOVmZlVmhOVmZlVmhOVmZlVmhOVmZlV2v8HDNxTCTQfcDcAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c3f908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.title('Darcy velocity on the outflow boundary, x component (ft/day)')\n",
    "pyplot.xlabel('x', fontsize=16)\n",
    "pyplot.ylabel('y', fontsize=16)\n",
    "\n",
    "pyplot.plot(y, u[49,:], '--', linewidth=2)\n",
    "pyplot.plot(9.8425+y, u[:,49], '--', linewidth=2)\n",
    "u[:,49]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3991.53526449,  4697.23214482,  4427.94528366,  4213.10279637,\n",
       "        4056.60881842,  3935.0524005 ,  3835.61636196,  3758.7042195 ,\n",
       "        3678.83215419,  3624.28803091,  3574.58365565,  3512.86339586,\n",
       "        3477.97481989,  3446.47687207,  3393.90113729,  3365.99608357,\n",
       "        3346.67520352,  3314.63917316,  3278.95525446,  3263.89905598,\n",
       "        3249.45519765,  3219.13221798,  3199.8855574 ,  3188.04140691,\n",
       "        3175.46727089,  3157.2693224 ,  3144.22991707,  3132.72142655,\n",
       "        3123.92635749,  3118.96690841,  3108.32799206,  3098.56287193,\n",
       "        3101.19357179,  3101.62102519,  3094.55662065,  3093.65095622,\n",
       "        3110.17941018,  3111.68331515,  3115.93189462,  3141.31882334,\n",
       "        3156.43775968,  3175.3078    ,  3215.96547724,  3250.99279038,\n",
       "        3305.71980505,  3375.05945207,  3470.19304338,  3601.64257033,\n",
       "        3743.87730196,  3061.23226415])"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEKCAYAAAAxXHOuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYmZmVfX/AK3XNwu/Lu7oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bb29208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.title('Darcy velocity on the outflow boundary, y component (ft/day)')\n",
    "\n",
    "pyplot.plot(y, v[:,49], '--', linewidth=2)\n",
    "pyplot.plot(9.8425+y, v[49,:], '--', linewidth=2)\n",
    "v[49,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
